Design of mixed analog/digital interface circuits for ... · Design of mixed analog/digital interface circuits for sensors and micro-systems ... in this thesis is the design of integrated

UNIVERSITA’ DEGLI STUDI DI PAVIA FACOLTA’ DI INGEGNERIA

DOTTORATO DI RICERCA IN

INGEGNERIA ELETTRONICA, INFORMATICA ED ELETTRICA

XIX CICLO

Design of mixed analog/digital interface circuits for sensors

and micro-systems

Relatore: Chiar.mo Prof Piero Malcovati

Tesi di dottorato di Andrea Rossini

Anno accademico 2005-2006

A mio papà Domenico e a mia mamma Tilde…

…una sola parola: grazie per tutto

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Index Index............................................................................................... 4

Abstract .......................................................................................... 7 Chapter 1 Introduction ............................................. 9

Introduction................................................................................................................ 9 The light ................................................................................................................... 12 Gamma rays ............................................................................................................. 16 X rays ....................................................................................................................... 18

Photographic plate.....................................................................................................................20 Photostimulable Phosphors (PSPs) ...........................................................................................20 Geiger counter ...........................................................................................................................20 Scintillators ................................................................................................................................21 Image Intensification..................................................................................................................21 Direct Semiconductor Detectors................................................................................................21

Radio astronomy ...................................................................................................... 22 The acquisition chain ............................................................................................... 22 Design overview ...................................................................................................... 23

Chapter 2 The Read-Out Integrated Circuit ............... 25

Introduction.............................................................................................................. 25 The LFDR project .................................................................................................... 29 The ROIC chip ......................................................................................................... 31

The analog acquisition chain.....................................................................................................32 The digital processing chain ......................................................................................................34 The pipeline converter ...............................................................................................................35 The Wilkinson converter ............................................................................................................36

Layout of the test chip.............................................................................................. 37 Chapter 3 The Wilkinson A/D Converter .................. 41

Introduction.............................................................................................................. 41 The basic blocks....................................................................................................... 43

Ramp generator..........................................................................................................................43 Voltage Comparator ..................................................................................................................44 The clock generator ...................................................................................................................47 The counter and the output register...........................................................................................47

Measurement Setup.................................................................................................. 47 Experimental results................................................................................................. 49 Conclusions.............................................................................................................. 54

Chapter 4 Clock-less Pipeline-Like A/D Converter.... 56

Introduction.............................................................................................................. 56 Error on the gain of the amplifier..............................................................................................57 Error on the DAC level ..............................................................................................................59

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Error in the comparator threshold ............................................................................................59 Basic blocks ............................................................................................................. 63

The ADC.....................................................................................................................................64 The residue amplifier .................................................................................................................65 The DAC.....................................................................................................................................68 The EOC generator....................................................................................................................69

Simulation Results ................................................................................................... 70 Experimental results................................................................................................. 76 Conclusions.............................................................................................................. 79

Chapter 5 Magnetic Sensor Models .......................... 81 Introduction.............................................................................................................. 81 Types of magnetic sensors ....................................................................................... 82

Squid ..........................................................................................................................................83 Search-coil .................................................................................................................................83 Magneto-inductive sensor ..........................................................................................................83 Magneto-resistance....................................................................................................................84 Hall sensor .................................................................................................................................88 Fluxgate .....................................................................................................................................90

The magnetic sensor model ..................................................................................... 94 Magneto-resistance....................................................................................................................96 Fluxgate .....................................................................................................................................97 Hall sensor .................................................................................................................................98 Magneto-inductance...................................................................................................................99 Temperature drift .....................................................................................................................101 Parasitic effects........................................................................................................................102 Dispersed field .........................................................................................................................103 Hysteresis .................................................................................................................................104

Experimental results............................................................................................... 106 Conclusions............................................................................................................ 109

Chapter 6 Interface Circuit for Fluxgate Magnetic

Sensor ................................................................................. 112 The interface circuit design.................................................................................... 112

Timing Block ............................................................................................................................114 Excitation Block .......................................................................................................................115 Read-Out Block ........................................................................................................................117 The ADC...................................................................................................................................118

Simulation Results ................................................................................................. 118 Layout .................................................................................................................... 121 Experimental results............................................................................................... 122 Conclusions............................................................................................................ 126

Bibliography............................................................................... 129

Acknowledgments/Ringraziamenti ............................................ 134

6

Ph.D thesis, Andrea Rossini 7

Abstract Scope of the work described in this thesis is the design of integrated interface circuits for microsensors.

This work can be divided into two parts. The first part describes the interface circuit for an array of X-

ray detectors, which is part of the acquisition chain for an X-ray spectrometer, that will be one of the

instruments used for the space mission towards Mercury “Bepi Columbus” of the European Space

Agency (ESA). More specifically a 10 bit clock-less pipeline-like A/D converter and a 10 bit

Wilkinson A/D converter have been designed, implemented and characterized. Moreover, we

implemented the complete interface circuit for a 16x16 array of X-ray detectors based on the Wilkinson

A/D converter. The complete acquisition chain achieves ±0.2 LSB of differential non-linearity, ±3.3

LSB of integral non-linearity and 34 electrons rms of input referred noise, consuming less than 1 mW

per channel.

The second part of the thesis deals with the interface circuit for an integrated fluxgate magnetic sensor

for electronic compass applications. The circuit can provide a widely programmable excitation current

to the fluxgate sensor and read-out the sensor signal, providing digital output. The interface circuit,

together with an integrated fluxgate sensor, achieves an angular error in detecting the Earth magnetic

field as low as 4 degrees.

This thesis is structured in the following way:

Chapter 1 Introduction

This chapter provides an overview of the integrated interface circuits for microsensors as well

as all the background information about X-ray detectors and detection systems.

Chapter 2 The Read Out Integrated Circuit

This chapter describes an interface circuit for an array of X-ray detectors. The project, named

Large Format Detector Readout, is funded by ESA (European Space Agency) and is carried-out

by University of Pavia, Polytechnic of Milano and Alcatel Alenia Space LABEN.

Chapter 3 The Wilkinson A/D converter


This chapter describes the first solution for implementing the A/D conversion in the read-out

integrated circuit described in Chapter 2: a 10-bit Wilkinson A/D converter. All the design and

implementation details are presented together with experimental results.

Chapter 4 Clock-less Pipeline- like A/D converter

This chapter describes the second solution for implementing the A/D conversion in the read-out

integrated circuit described in Chapter 2: a 10-bit clock-less pipeline-like A/D converter. The

peculiarity of this converter is the absence of the clock. The circuit, indeed, acts as a

combinatory logic, thus avoiding the presence of a synchronization. All the design and


Chapter 5 Magnetic sensor models

This chapter provides the background information about magnetic sensors. Moreover, it

describes the magnetic sensors model that have been developed to simulate the behaviour of

four different types of sensors: an hall devices, a magneto-resistance, a flux-gate and a magneto-

inductance. These models have been used to design the interface circuit for fluxgate magnetic

sensors described in Chapter 6.

Chapter 6 Interface circuit for fluxgate magnetic sensor

This chapter describes the interface circuit for fluxgate magnetic sensors. The circuit can

provide a widely programmable excitation current to the fluxgate sensor and read-out the sensor

signal with variable gain. Moreover, the circuit provides digital output. All the design and


Chapter 1 Interface circuits


Chapter 1

Introduction

1.1 Introduction

Computers can only handle zeros and ones and if we want to automate the procedure for sensing

any physical quantity in a repetitive mode, we need sensors to translate temperature, pressure,

vehicle speed or any other parameter into a form manageable by computers. This can be done with

appropriate interface circuits that translate the analog output of the sensors into the digital domain

of the computer.

Over the years, sensors have been fabricated by using many different technologies. At the

beginning, sensors were realized in a mechanical way, but, because of the success of the integrated

silicon technologies, also the sensor technology received renewed interest. Around 1965, Prof.

James Angell started a silicon sensor research group at Stanford University. One of his students,

Kensall Wise, started performing selective etching of structures and this marks the start of the

electronic sensors and the interface circuits as we know today. Many internationally well-known

universities in the USA, Europe and Japan were quick to follow.

Obviously, first applications of this sensors were bulky, costly and consuming a lot of power. But

following the development of the integrated circuit technology, new markets for these sensors were

found and even more new applications developed, thus increasing the effort to obtain even better

and cheaper sensors.

The main advantages of an electronic sensor are the lack of any mechanical part that can break, the

possibility to obtain a large number of pieces at the same price and with the same characteristics,

and, more importantly, the possibility to miniaturize the component itself step by step with the

evolution of the electronic integrated technologies.

Thus the need for sensors has grown rapidly in recent years for industrial control, automation and

consumer applications. In modern measurement and control systems, a large number of sensors

collect the information about the process variable in the system being monitored. They usually

provide analog information on the system being monitored through signal conditioning circuits

connected to a processor. The latter interprets the information, makes appropriate decisions most



likely in conjunction with higher-level control, and implements those decisions via actuators or it

displays the information to the human world.

When developing an interface circuit for sensors, the designer has to take into account several

aspects about not only the electronics itself, but also about the system and the sensor characteristics

as well as the environment where the sensor has to work.[1]

The first step in any sensing device design is to define what is to be sensed and how. The

identification of the physical parameter (quantity) to be sensed is not always obvious. An example

to build a flow meter consists in measuring the rotation of an impeller blade. In this case, it would

be easy to assume that what is to be sensed is the rotation of the impeller blade. In fact, fluid flow is

the desired quantity to be sensed. The limiting identification of the impeller blade motion as the

parameter to be sensed, reduces the possible design approaches and available technologies open to

the designer. In most cases, several methods of sensing a physical parameter can be identified. Each

of these methods will consist of a conceptual approach with an associated technology. The

conceptual approach describes how the sensing function might be implemented without considering

the engineering details and component specifications. At this level of detail, some conceptual

approaches can be immediately eliminated on the basis of cost, complexity, etc. Physical parameters

can often be sensed by using indirect methods: as an example, the requirement to sense temperature

changes. An obvious approach would be to use a thermocouple since it is a temperature sensor. An

alternative method can be a magnetic sensor glued over a bellows. An increase and/or decrease in

temperature causes the bellows to expand or contract, moving the attached magnet. The

corresponding change in magnetic field is sensed by a magnetic sensor, for instance a Hall device.

The final result turns out to be the conversion of the input temperature into a measurable electrical

field or into a current/voltage.

Once the most promising sensing techniques are identified, it is necessary to determine input and

output requirements, the major sensing device components, and the application requirements. For

example, which are the electrical characteristics of the output pulse required for the application

(current, voltage, rise time, fall time, etc.) and if the sensors are able to fulfil it. If the required

electrical characteristics are not met at the output of the sensors, it is necessary to find which

additional circuitry is required. The environmental requirements must also be identified. For

example, if a sensing device has to be used in oil laden air maybe to sense the level of oil in a car,

then an optical approach would be discarded. The strengths and weaknesses of each approach must

be weighed. The features and benefits of each technology must be evaluated with respect to the



considered application. During the preliminary feasibility study, it is important that all the key

informations are considered. Among them, we can mention:

• the overall cost;

• the volume productivity;

• the component availability;

• the sensor complexity;

• the tolerance of part-to-part variations;

• the compatibility with other system components;

• the reliability;

• the repeatability;

• the maintainability;

• the environmental constraints.

Although several of these considerations can not be quantified until a detailed design is completed,

they must, nevertheless, be weighed at this point.

An important consideration about the interface circuit and the sensor itself when exploiting the

possibility to use integrated circuits is the possibility to integrate both the interface circuit and

several sensors on the same chip or in the same package, leading to micro-systems or micro-

modules[2]. The potential advantages of this approach are: the cost of the measurement system is

strongly reduced thanks to batch fabrication of both sensors and interface circuits, its size and

interconnections are minimized, and its reliability is improved. However, the choice of materials

compatible with silicon IC technologies is limited and their properties are process-dependent.

Therefore, integrated sensors often show worse performance than their discrete counterparts due to

weak signals, offset and nonlinear transfer characteristics. This explains the increasing demand for

sensors interface. There are two possible approaches for implementing micro-sensor systems: the

micro-system approach and the micro-module approach. In the micro-system approach, the sensor

and the interface circuitry are integrated on the same chip. Therefore, a micro-sensor must be

designed by taking into account the material characteristics given by the standard IC process used.

Furthermore, it has to be considered that, when the standard IC fabrication flow is completed,

additional specific process steps are required in order to implement the sensor itself By exploiting

this approach it is worth to point out that cost and yield issues can rise, especially when using

modern technologies with small feature size. In fact, while the silicon area occupied by the interface



circuit is typically shrinking, together with the feature size of the technology, the sensor area in

most cases remains constant. Therefore, while for integrated circuits the increasing cost per unit

area is abundantly compensated by the reduction in silicon area, leading to an overall reduction of

chip cost with the technology feature size, this might not be true for integrated micro-systems.

Moreover, a defect in the sensors may force us to discard the complete micro-system, even if the

circuitry is working, thereby lowering the yield and again increasing the overall cost. It has to be

underline that parasitic elements due to the interconnections between the sensor and the interface

circuitry are minimized and, more importantly, are well-defined and reproducible. In addition, the

system assembly is simple, inexpensive, and independent of the number of connections needed,

since all the interconnections are implemented during the IC fabrication process. Finally, when

required, the use of the same technology allows us achieving good matching performances between

elements of the sensor and of the interface circuitry. This way accurate compensation of many

parasitic effects can be obtained.

In the micro-module approach, the sensors and the interface circuitry are integrated in different

chips. They are included in the same package or mounted on the same substrate, The

interconnections between the sensor chip and the interface circuit chip can be realized with bonding

wires or other techniques. The two chips can be fabricated with two different technologies, which

are optimized for the sensors and the circuitry. The sensor designer can adjust the material

properties of the technology to optimize the performance of the device, and cost and yield issues

mentioned for the micro-system approach are no longer a concern. However, also the micro-module

approach has a number of drawbacks. First, the assembling of the system can be quite expensive

and a source of possible failures, the number of interconnections allowed between the sensor and

the circuitry is limited. Moreover, the parasitic elements due to the interconnections are some orders

of magnitude larger, more unpredictable, and less repeatable than in the micro system approach,

thus limiting in many cases the effectiveness of any improvements obtained in sensor performance

by technology optimization. Finally, matching between elements of the sensor and of the interface

circuitry cannot be guaranteed. The advantages and disadvantages of both approaches are

summarized in Table 1.1-1.



Table 1.1-1: Micro-system versus micro-module approach

In the next chapters we will present two interface circuits for sensors, designed for different

applications. One is for a X-ray spectrometer, while the second is for an electronic compass. In the

following paragraphs we will provide some background information on X-ray detectors, while the

state of the art on magnetic sensors is described in chapter 6.

1.2 The light

The only thing that can flow between planets is light. The light is a kind of electromagnetic

radiation, but it is not the only one. In particular, the light as we know is part of the spectrum, that

means the range of all possible electromagnetic radiations that exist in the universe. The visible

spectrum is the portion of the electromagnetic spectrum that is visible to the human eye.

Electromagnetic radiation in this range of wavelengths is called visible light or simply light. There

are no exact bounds to the visible spectrum; a typical human eye will respond to wavelengths from

400 nm to 700 nm. From Figure 1.2-1 it is possible to realize that light as we know it is only a small

part of the total radiation available. If we were able to see all the spectrum we could have more and

more information about all the space around us, but not only, they can give us the tools to built new

applications and open new worlds. The effect of research in this field are various, some simple

applications like television, mobile phone, medical device for radiography, can give and idea how

knowledge over this field is important . This is what scientists correctly though at the beginnings

and explain why research in this field is so much important.

Micro-system Approach Micro-module Approach

+ Reliability + Optimal yield

+ Minimal interconnection parasitic + Optimal process both for sensor and

circuitry

+ Simple and inexpensive assembly + Cost that scales with feature size

- Reduced yield - Reliability

- Cost that doesn’t scales with feature size - Large interconnection parasitic

- Optimal process only for sensor - Expensive assembly



Nearly all objects in the universe emit, reflect and/or transmit some light. The distribution of this

light along the electromagnetic spectrum (called the spectrum of the object) is determined by the

object composition. Several types of spectra can be distinguished depending upon the nature of the

radiation coming from an object:

• if the spectrum is composed primarily of thermal radiation emitted by the object itself, an

emission spectrum occurs;

• some bodies emit more or less light according to the blackbody spectrum;

• if the spectrum is composed of background light, and the object is able to transmit some part

of it or to absorb it, then an absorption spectrum occurs.

The light is thus composed by different radiations, gamma ray, X-ray ultra-violet, infrared,

microwaves and radiowaves, as shown in Figure 1.2-1.

Figure 1.2-1: Electromagnetic radiation spectrum

Radiowaves generally are used by antennas of appropriate size (according to the principle of

resonance), with wavelengths ranging from hundreds of meters to about hundreds of millimetres.

They are used for data transmission. Television, mobile phones, wireless networking and amateur

radio all use radiowaves.

The super high frequency (SHF) and extremely high frequency (EHF) of Microwaves come next up

the frequency scale. Microwaves are waves which are typically short enough to employ tubular

metal waveguides of reasonable diameter. Microwave energy is produced with klystron and

magnetron tubes, and with solid state diodes such as Gunn and IMPATT devices. Microwaves are

absorbed by molecules that have a dipole moment in liquids. In a microwave oven, this effect is

used to heat food. Low-intensity microwave radiation is used in Wi-Fi internet connection.

Terahertz radiation is a region of the spectrum between far infrared and microwaves. Until now, the

range has been rarely studied and few sources existed for microwave energy at the high end of the



band (sub-millimetre waves or so-called terahertz waves), but applications such as imaging and

communications are now appearing. Scientists are also looking to apply Terahertz technology in the

armed forces, where high frequency waves might be directed at enemy troops to incapacitate their

electronic equipments.

The infrared part of the electromagnetic spectrum covers the range from roughly 300 GHz (1 mm)

to 400 THz (750 nm). It can be divided into three parts:

• Far-infrared, from 300 GHz (1 mm) to 30 THz (10 μm). The lower part of this range may

also be called microwaves. This radiation is typically absorbed by so-called rotational

modes in gas-phase molecules, by molecular motions in liquids, and by phonons in

solids. The water in the Earth's atmosphere absorbs so strongly in this range that it

renders the atmosphere effectively opaque. However, there are certain wavelength

ranges ("windows") within the opaque range which allow partial transmission, and can

be used for astronomy. The wavelength range from approximately 200 μm up to a few

mm is often referred to as "sub-millimetre" in astronomy, reserving far infrared for

wavelengths below 200 μm.

• Mid-infrared, from 30 to 120 THz (10 to 2.5 μm). Hot objects (black-body radiators) can

radiate strongly in this range. It is absorbed by molecular vibrations, i.e. when different

atoms in a molecule vibrate around their equilibrium positions. This range is sometimes

called the fingerprint region since the mid-infrared absorption spectrum of a compound

is very specific for that compound.

• Near-infrared, from 120 to 400 THz (2500 nm to 750 nm). Physical processes that are

relevant for this range are similar to those for visible light.

By moving to higher frequencies (Figure 1.2-1) the ultraviolet region can be found. The wavelength

of the radiation in this zone is shorter than the violet end of the visible spectrum.

Being very energetic, UV can break chemical bonds, making molecules unusually reactive or

ionizing them, in general changing their mutual behaviour. Sunburn, for example, is caused by the

disruptive effects of UV radiation on skin cells, which can even cause skin cancer, if the radiation

damages the complex DNA molecules in the cells (UV radiation is a proven mutagen). The Sun

emits a large amount of UV radiations, which could quickly turn Earth into a barren desert, but

most of them are absorbed by the atmosphere ozone layer before reaching the planet surface.



After UV we have the X-rays. Hard X-rays have shorter wavelengths than soft X-rays. Sources of

X-rays are stars, and strongly some types of nebulae, Neutron stars and accretion disks around black

holes. X-rays are used for they ability to pass through many substances, and this feature makes them

useful in medicine and industry fields. An X-ray machine works by firing a beam of electrons at a

"target". If the electron beam intensity is adequate, X-rays can be produced.

In Figure 1.2-1, hard X-rays are followed by gamma rays. These are the most energetic photons,

having no lower limits to their wavelength. They are useful to astronomers in the study of high-

energy objects or regions and they are used by physicists, thanks to their penetrative ability and

thanks to the possibility to be produced from radioisotopes. The wavelength of gamma rays can be

measured with high accuracy by means of Compton scattering.

Note that there are no defined boundaries between the types of electromagnetic radiations. Some

wavelength is characterized by properties typical of two regions of the spectrum. For example, red

light resembles infra-red radiation since it can be able to resonate some chemical bonds. At the

same times there is an overlap between the hard X-rays and the gamma rays that doesn’t permit to

define a sharp limit between the two radiation.

Since this two last form of radiation are the highest-energy end of the electromagnetic spectrum, we

will focus our attention on their nature, their possible applications and how to detect them.

1.3 Gamma rays

As explained in the previous paragraph, gamma rays form the highest-energy end of the

electromagnetic spectrum. They are often defined to begin at an energy of 10 keV, corresponding to

a minimum frequency of 2.42 EHz (ExaHertz or 1018 Hertz), or a maximum wavelength of 124 pm,

although electromagnetic radiation from around 10 keV to several hundred keV is also referred to

as hard X-rays. It is important to note that there is no physical difference between gamma rays and

X-rays of the same energy. They are two names for the same electromagnetic radiation, just like

sunlight and moonlight are two names for visible light. Rather, gamma rays are distinguished from

X-rays by their origin. Gamma ray is a term for high-energy electromagnetic radiation produced by

nuclear transitions, while X-ray is a term for high-energy electromagnetic radiation produced by

energy transitions due to accelerating electrons. Since it is possible for some electron transition to

be of higher energy than some nuclear transitions, there is an overlap region between what it is

referred to as low energy gamma rays and high energy X-rays.



Gamma rays are a form of ionizing radiation; they are more penetrating than either alpha or beta

radiation (neither of which is electromagnetic radiation), but less ionizing. For instance, a gamma

ray will pass through 1 cm of aluminium, while an alpha particle will be stopped by even a single

sheet of paper. Gamma sources are used for a number of applications in both medicine and industry

field.

When a gamma ray passes through matter, its probability of absorption is proportional to the

thickness of the layer itself. In passing through matter, gamma radiation ionizes via three main

processes: the photoelectric effect, Compton scattering, and pair production, [3], [4], [5].

• Photoelectric Effect: This describes the case in which a gamma photon interacts with atoms

and transfers its energy to an atomic electron, ejecting that electron from the atom. The

kinetic energy of the resulting photoelectron is equal to the energy of the incident

gamma photon minus the binding energy of the electron. The photoelectric effect is the

dominant energy transfer mechanism for X-ray and gamma ray photons with energies

below 50 keV, but it is much less important at higher energies.

• Compton Scattering: This is an interaction in which an incident gamma photon transfers

enough energy to an atomic electron to cause its ejection, with the remainder of the

original photon's energy being emitted as a new, lower energy gamma photon with an

emission direction different from that of the incident gamma photon. The probability of

Compton scatter decreases with increasing photon energy. Compton scattering is

considered to be the main absorption mechanism for gamma rays in the intermediate

energy range (100 keV to 10 MeV). It can be interesting to note that such an energy

spectrum includes most gamma radiations present in a nuclear explosion. Compton

scattering is relatively independent of the atomic number of the absorbing material.

• Pair Production: By interaction via the Coulomb force, in the proximity of the nucleus, the

energy of the incident photon is spontaneously converted into the mass of an electron-

positron pair. A positron is the anti-matter equivalent of an electron; it has the same

mass as an electron, but it has a positive charge equal in strength to the negative charge

of an electron. Energy in excess of the equivalent rest mass of the two particles

(1.02 MeV) appears as the kinetic energy of the pair and the recoil nucleus. The positron

has a very short lifetime (about 10-8 seconds). The positron combines with a free

electron and the entire mass of these two particles is then converted into two gamma

photons of 0.51 MeV energy each.



The secondary electrons (or positrons) produced in any of these three processes frequently have

enough energy to produce many ionizations up to the end of range.

The nature of gamma rays makes them useful to kill bacteria in medical equipments sterilization

process. They are also used to kill insects in foodstuffs, particularly meat, marshmallows, pies,

eggs, and vegetables.

Due to their tissue penetrating property, gamma rays and X-rays have a wide variety of medical

uses such as in CT (Computerised Tomography) Scans and radiation therapy. However, as a form

of ionizing radiation, they have the capability to produce molecular changes, particularly to DNA,

giving them the potential to cause cancer.

Despite their cancer-causing properties, gamma rays are also used to treat some types of cancer. In

the procedure called gamma-knife surgery, multiple concentrated beams of gamma rays are directed

on the growth in order to kill the cancerous cells. The beams are aimed from different angles to

focus the radiation on the growth while minimizing damages to the surrounding tissues.

Gamma rays are also used for diagnostic purposes in nuclear medicine. Several gamma-emitting

radioisotopes are used in such a field. One of these is technetium-99m. When administered to a

patient, a gamma camera can be used to form an image of the radioisotope distribution by detecting

the gamma radiation emitted. Such a technique can be employed to diagnose a wide range of

conditions (e.g. spread of cancer to the bones).

Gamma ray detectors are also starting to be used in Pakistan as part of the Container Security

Initiative (CSI). These machines are advertised to scan 30 containers per hour. The objective of this

technique is to pre-screen merchant ship containers before they enter ports.

1.4 X rays

X-rays are a form of electromagnetic radiation with a wavelength in the range from 10 to 0.01 nm,

corresponding to frequencies in the range from 30 to 30000 PHz (1015 Hertz). X-rays are primarily

used for diagnostic radiography and crystallography. X-rays are a form of ionizing radiation and as

such can be dangerous.

When medical X-rays are being produced, a thin metallic sheet is placed between the emitter and

the target, effectively filtering out the lower energy (soft) X-rays. The resultant X-ray is called hard.

Soft X-rays overlap the range of extreme ultraviolet. The frequency of hard X-rays is higher than

the one of soft X-rays, and the wavelength is shorter. Hard X-rays overlap the range of "long"-



wavelength (lower energy) gamma rays. However, the distinction between the two terms depends

on the source of the radiation, not on its wavelength. X-ray photons are generated by energetic

electron processes, while gamma rays by transitions within atomic nuclei.

The basic production of X-rays is obtained by accelerating electrons in order to collide with a metal

target (usually tungsten, but sometimes molybdenum). Here the electrons suddenly decelerate upon

colliding with the metal target and, if the electron is energetic enough, it is able to knock out an

electron from the inner shell of the metal atom. As a result, electrons from higher energy levels fill

up the vacancy and X-ray photons are emitted. This causes the spectral line part of the wavelength

distribution. There is also a continuum bremsstrahlung component given off by the electrons as they

are scattered by the strong electric field near the high Z (proton number) nuclei.

Nowadays, for many (non medical) applications, X-ray production is achieved by synchrotrons.

The detection of X-rays is based on various methods. The most popular methods are a photographic

plate, X-ray film in a cassette, and rare earth screens.

1.4.1 Photographic plate

The X-ray photographic plate or film is used in hospitals to produce images of the internal organs

and bones of a patient. The part of the patient to be X-rayed is placed between the X-ray source and

the photographic receptor to produce what is a shadow of all the internal structures of that particular

part of the body being X-rayed. The X-rays are blocked by dense tissues such as bone and pass

through soft tissues. Those areas where the X-rays strike the photographic receptor turn black when

it is developed. So where the X-rays pass through "soft" parts of the body such as organs, muscles,

and skin, the plate or film turns out to be black. Contrast compounds containing barium or iodine,

which are radiopaque, can be injected in the artery of a particular organ, or given intravenously. The

contrast compounds essentially block the X-rays and, hence, the circulation of the organ can be

more readily seen. For some procedures, the contrast can have a syrupy consistency, which can be

thinned by warming, and is introduced with a power injector, such as the Nemoto Injector.



1.4.2 Photostimulable Phosphors (PSPs)

An increasingly common method of detecting X-rays is the use of Photostimulable Luminescence

(PSL), pioneered by Fuji in the 1980's. In modern hospitals a PSP plate is used instead of the

photographic plate. After the plate is X-rayed, excited electrons in the phosphor material remain

'trapped' in 'colour centres' in the crystal lattice until stimulated by a laser beam passed over the

plate surface. The light given off during laser stimulation is collected by a photomultiplier tube and

the resulting signal is converted into a digital image by computer technology, which gives this

process its common name, computed radiography. The PSP plate can be used over and over again.

1.4.3 Geiger counter

Initially, most common detection methods were based on the ionisation of gases, as in the Geiger-

Müller counter: a sealed volume, usually a cylinder, with a polymer or thin metal window contains

a gas, and a wire. An high voltage is applied between the cylinder (cathode) and the wire (anode).

When an X-ray photon enters the cylinder, it ionises the gas. These ions accelerate toward the

anode, in the process causing further ionisation along their trajectory. This process, known as an

avalanche, is detected as a sudden flow of current, called a "count" or "event". Finally, the electrons

form a virtual cathode around the anode wire drastically reducing the electric field in the outer

portions of the tube. This halts the collision ionizations and limits further growth of avalanches. As

a result, all "counts" on a Geiger counter are the same size and it can give no indication as to the

particle energy of the radiation, unlike the proportional counter. The intensity of the radiation is

measurable by the Geiger counter as the counting-rate of the system.

In order to gain energy spectrum information a diffracting crystal may be used to first separate the

different photons. The method is called wavelength dispersive X-ray spectroscopy (WDX or WDS).

Position-sensitive detectors are often used in conjunction with dispersive elements. Other inherently

energy-resolving detection equipments may be used, such as the aforementioned proportional

counters. In either case, the use of suitable pulse-processing (MCA) equipments allows digital

spectra to be created for later analysis.

For many applications, counters are not sealed but are constantly fed with purified gas (thus

reducing problems of contamination or gas aging). These are called "flow counter".



1.4.4 Scintillators

Some materials such as sodium iodide (NaI) can "convert" an X-ray photon to a visible photon; an

electronic detector can be built by adding a photomultiplier. These detectors are called

"scintillators", filmscreens or "scintillation counters". The main advantage of using these is that an

adequate image can be obtained while subjecting the patient to a much lower dose of X-rays.

1.4.5 Image Intensification

X-rays are also used in "real-time" procedures such as angiography or contrast studies of the hollow

organs (e.g. barium enema of the small or large intestine) using fluoroscopy acquired with an image

intensifier. Angioplasty, medical interventions of the arterial system, rely heavily on X-ray-sensitive

contrast to identify potentially treatable lesions.

1.4.6 Direct Semiconductor Detectors

Since 1970s, new semiconductor detectors have been developed (silicon or germanium doped with

lithium, Si(Li) or Ge(Li)). X-ray photons are converted to electron-hole pairs in the semiconductor

and are collected to detect the X-rays. When the temperature is low enough (the detector is cooled

by Peltier effect or better by liquid nitrogen), it is possible to directly determine the X-ray energy

spectrum. This method is called energy dispersive X-ray spectroscopy (EDX or EDS) and it is often

used in small X-ray fluorescence spectrometers. These detectors are sometimes called "solid

detectors". Cadmium telluride (CdTe) and its alloy with zinc, cadmium zinc telluride detectors have

an increased sensitivity, which allows lower doses of X-rays to be used. These kind of detectors

have not been adopted in practical applications in Medical Imaging until 1990's. Actually,

amorphous selenium is used in commercial large area flat panel X-ray detectors for chest

radiography and mammography. Silicon drift detectors (SDDs), produced by conventional

semiconductor fabrication process, now provide a cost-effective and high resolving radiation

measurement. They replace conventional X-ray detectors, such as Si(Li)s, since liquid nitrogen

cooling process is not required.



1.5 Radio astronomy

Radio astronomy is the field in which study of gamma and x-rays is most helpful. Radio astronomy

is the study of celestial phenomena through measurements of the characteristics of radio waves

emitted by physical processes occurring in the space. In order to receive good signals, radio

astronomy requires large antennas, or arrays of smaller antennas all working together (the Very

Large Array near Socorro, New Mexico can be taken such an example). Most radio telescopes use a

parabolic dish to reflect the waves to a receiver which, in turn, detects and amplifies the signal into

usable data. This allows astronomers to see a region of the radio sky. By taking multiple scans of

overlapping strips of the sky it is possible to reconstruct an image ('mosaicing'). Radio astronomy is

a relatively new field of astronomical research that still has much more to be discovered.

Radio astronomy has led to substantial increases in astronomical knowledge, particularly with the

discovery of several classes of new objects, including pulsars, quasars and radio galaxies. This has

been possible since radio astronomy allows detecting things that are not observable in optical

astronomy. Radio astronomy is also partly responsible of the idea that dark matter is an important

component of our universe. Radio measurements of the rotation of galaxies suggest that there is

much more mass in galaxies than the one which has been directly observed (see Vera Rubin). The

cosmic microwave background radiation was also first detected using radio telescopes. However,

radio telescopes have also been used to investigate objects much closer to our planet, including

observations of the Sun and solar activity, together with planets radar mapping.

Radio telescopes can now be found all over the world. Widely separated telescopes are often

combined using a technique called interferometry in order to obtain observations with much higher

resolution with respect the one that could be achieved by using a single receiver. Initially,

telescopes within a few kilometres of each other were combined (see, for example, the Mullard

Radio Astronomy Observatory), but since the 1970s telescopes from all over the world (and even in

Earth orbit) have been combined to perform Very Long Baseline Interferometry.

1.6 The acquisition chain

Once the X-rays or gamma rays are detected and the proportional electrical signal is available, it is

necessary to develop all the circuitry to process the signal itself. In a typical acquisition chain,

shown in Figure 1.6-1, the charge produced by the detector is similar to a current Dirac delta. This



charge is integrated in a charge sensitive amplifier (CSA), that produces a voltage step at the output

equal to the charge divided by the feedback capacitor. This voltage step is filtered by means of a

band-pass filter (or pulse shaper amplifier, PSA) in order to reduce the noise and increase the

signal-to-noise ratio (SNR). The resulting output is a waveform whose peak value is related to the

area of the detector current pulse and, hence, to the energy absorbed. The peak of the signal

typically occurs after few microseconds (depending on the characteristics of the filter). The useful

information for the data processing is therefore contained in the peak value of the signal obtained at

the output of PSA. In order to digitize this value, the analog chain requires a specific circuit that

detects the peak and then stores it for the A/D conversion,[6] ,[7].

Figure 1.6-1: The acquisition chain

1.7 Design overview

In the next chapters we will describe an interface circuit for X-rays spectrometry called “Read-Out

Integrated Circuit” (ROIC). The circuit has been developed in the frame of a project of the

European Space Agency (ESA) named LFDR, Large Format Detector Read-out, for the detection of

X-rays emitted by the Sun and reflected by the surface of Mercury. This project is scheduled to be

used in the ‘Bepi Colombo’ space ship. The Mercury mission, proposed in May 1993 and accepted

by the Scientific Program Commission (SPC) in September 1999, takes its name form the Italian

scientist Giuseppe Colombo (1920-1984), first man to explain the peculiar rotation of Mercury, that

rotates three times on itself every two rotations around the Sun. The expect lift-off date is planned

for August 2009, while the land on the surface of Mercury is scheduled for October 2012. The

activity of design of the ASIC is a collaboration between the University of Pavia, Laben Alenia

Alcatel Spazio and Politecnico di Milano, [8].



Chapter 2 The Read-Out Integrated Circuit



In this chapter we describe an interface circuit for a matrix of X-rays sensors. The project,

named Large Format Detector Readout (LFDR), is founded by the European Space Agency

(ESA) in cooperation with University of Pavia, Politecnico di Milano and Laben Alcatel Alenia

Space.

2.1 Introduction

The ROIC (Read-Out Integrated Circuit) chip is an ASIC (Application Specific Integrated

Circuit) designed to read out signals coming from X-ray detectors implemented in a X-ray

spectrometer. The latter will be one of the instruments used in the Mercury spatial mission

named “Bepi Colombo”, Figure 2.1-1.

Figure 2.1-1 : The probe for the Mercury mission



The Mercury mission, proposed in May 1993 and accepted by the Scientific Program

Commission (SPC) in September 1999, takes its name form the Italian scientist Giuseppe

Colombo (1920-1984), first man to explain the peculiar rotation of Mercury, that rotates three

times on itself every two rotations around the Sun. The expected lift-off date is planned for

August 2009, while the land on the surface of Mercury is scheduled for October 2012.

The mass of the space ship at the take-off will be approximately 1500 kg while it is previewed

that at the arrival on the planet surface it will be about 1100 kg. Mercury is the nearest planet to

the Sun and its exploration should give important informations about the origins of the solar

system.

Mercury, Venus, Earth and Mars form the ‘terrestrial planet family’ and everyone carries

essential informations to reconstruct the history of this group. Knowing their origins and their

evolution is a milestone to know how the conditions necessary for life in our solar system have

been developed and if it is possible to find the same conditions in other systems. Moreover,

thanks to the proximity to the Sun, it will be possible to investigate the validity of the Einstein

gravity theory.

After one year of theoretical studies about the technologies and the system finished in 1999, the

best approach to reach the proposed goals is to send two Orbiters and one Lander:

• The Mercury Planetary Orbiter (MPO), shown in Figure 2.1-2, is a module that will

be in a low orbit at the nadir to observe and relieve the planet.

Figure 2.1-2: The Mercury Planetary Orbiter (MPO) module



• The Mercury Magnetospheric Orbiter (MMO), shown in Figure 2.1-3, is a module

that will be on an eccentric orbit, carrying the largest part of instruments for magnetic

fields, waves and particles detection.

Figure 2.1-3: The Mercury Magnetospheric Orbiter (MMO)module

• The Mercury Surface Element (MSE) lander, shown in Figure 2.1-4, will land on the

planet to observe and study the chemical-physical composition of the surface.

Figure 2.1-4: The Mercury Surface Element (MSE) module

In Figure 2.1-5 is shown the Bepi Colombo probe. In this figure, it is possible to note the

Mercury Planetary Orbiter (MPO) and the stage of electrical propulsion module (SEPM) linked



with the Chemical Propulsion Module (CPM). In the background are depicted the Mercury

Magnetospheric Orbiter (MMO) and the Mercury Surface Element (MSE).

Figure 2.1-5: The Bepi Colombo probe

The interplanetary journey will use an electrical propulsion system fed by solar energy. This

module will be unfastened at the arrival on the planet surface. The Chemical Propulsion Module

(CPM) will be used for all the movements necessary to reach the established orbit. Finally, after

that all the elements are ready and out of the probe, including MSE, the CPM will be unfastened

as well.

The basic idea is to divide the ship elements in two structures that use propulsion engines really

similar. The ship is divided into modules and it is suitable for a large variety of choices

compatible with the objective of the mission.



2.2 The LFDR project

During the last few years, the European Space Agency (ESA) has developed new types of

detectors for X-rays based on composed semiconductor like the Gallium Arsenide (GaAs),

Cadmium Zinc Telluride (CdZnTe) or other compounds based on covalent ties between an

element of the 5th group and one of the 3rd,[9]. The high quality of the increased epitassial layers

allows the survey of X-rays with an energetic resolution near the Fano’s limit, which is the

statistical limit of the bearers number created from the absorption process of photons. The

epitassial states development has been initially critical so that first sensors based on the new

crystalline structures included only single detectors. Presently, the fabrication process is mature

and medium size arrays of sensors (16 x 16, 32 x 32, 64 x 64 with pixel of area equal to 300 x

300 μm2) have been realized. Till now, the sensors have been read-out using traditional analog

acquisition chains, made with discrete elements. In order to make possible the use of such a

detectors in space environment, reduced dimensions and electronic power consumption are

required. The development of custom ASICs for signal processing is mandatory. Moreover, in

order to decrease the parasitic capacitances due to the interconnection between the sensor and

the acquisition system, it has been chosen to connect this two parts by employing the bump-

bonding technique.

The sensor characteristics are summarized in Table 2.2-1.

Composition GaAs

Pixel pitch 300 x 300 μm2

Pixel size 250 x 250 μm2

Array sizes 16 x 16, 32 x 32, 64 x 64

Detection energy range 0.5-50 keV

Efficiency of absorption ≥ 90%

Leakage current 2 pA/pixel

Energetic resolution (typical)

@ 7 keV

@ 50 keV

250 eV

400 eV



Pixel capacitance (estimated) ≈ 200 fF

Bump bond capacitance (estimated) ≈ 200 fF

Table 2.2-1: Sensor characteristics

The detector has been realized with an epitassial layer of elevated purity, grown over n-type

substrate. The thickness of the layer can vary from a minimum of some tens of micrometers until

some hundreds of micrometers. A microphotograph of a matrix formed by 32 x 32 elements

(12x12 mm2) in Gallium Arsenide with pixel size 250 x 250 μm2 (pitch of 300 x 300 μm2) is

shown in Figure 2.2-1.

Figure 2.2-1: Microphotography of 32x32 matrix

The main characteristics of the read-out electronics are:

• input referred noise lower than 30e-;

• power consumption lower than 1 mW/channel;

• shaping time programmable from 1 μs to 10 μs;

• resolution A/D higher than 9 bit

• INL lower than ±2LSB

• DNL lower than ±0.25LSB

• signal frequency 104 events/s for single pixel and 106 events/s for the all array;

• input range from 120 electrons up to 12000 electrons;

• possibility to handle positive and negative signals;

• sensor maximum leakage current equal to ±150 pA/pixel;

• power supply equal to 3.3 V.

To achieve the characteristics described above, a 0.35-μm four metal two poly-silicon CMOS

technology has been adopted.



To connect with the bump-bonding technique the sensor and the acquisition chain it has been

necessary to space the front-end channels on the read-out chip of 300 μm in each directions. This

spacing is due to the geometry of the detector array. Moreover it has been made possible to

disable the charge preamplifier and the trigger generator for every single channel in order to

perform evaluations about the linearity of a single channel without any noise due to the cross-

talk among channels or eliminate from the matrix damaged or non-working pixels.

2.3 The ROIC chip

The block diagram of the ROIC (Read-Out Integrated Circuit) is shown in Figure 2.3-1.

Figure 2.3-1: The ROIC integrated circuit block diagram

The chip is composed by two main blocks, [11]:

• the analog acquisition chain;

• the digital process chain.



When the two blocks are connected, we obtain the structure on which is based the ROIC

ASIC,[10].

The ROIC basic circuits are:

• low noise charge amplifier for each pixel detector;

• a pulse shaper for each pixel detector;

• a peak and hold circuit for each pixel detector;

• a multiplexing circuit between the output of the peak and hold circuit and the A/D

converter;

• a threshold detect circuit for each pixel;

• a digital circuit to identify the pixel that detected the event;

• an acquisition interface;

• the A/D converter;

• a digital circuit to enable/disable each single channel and/or the charge pre-amplifier;

• a digital circuit to set parameters in the acquisition chain.

2.3.1 The analog acquisition chain

A block diagram of the circuit is shown in Figure 2.3-2. It can be divided in three main parts: the

front-end electronics (containing one readout cell per pixel), the backend electronics (including

the A/D converters) and the auxiliary services. The front-end part of the circuit consists of a

charge sensitive preamplifier, a second-order RC-CR shaper, a baseline restorer (BLR), a peak

stretcher and an output buffer. The charge preamplifier employs a PMOS transistor as input

device and an NMOS transistor to perform a continuous reset. A pole-zero compensation

network is present between the preamplifier and the first shaping stage. The second shaping

stage is realized with a current mode cell feeding the BLR. In order to achieve long shaping

times (up to 10 μs) within the pixel area constrain, a current conveyor technique has been

employed in both the shaping stages as well as in the BLR. The BLR feeds a high precision peak

stretcher. An output buffer is included to drive the signal line to the A/D converter. The power

consumption of this section is 380 μW.

In addition, the front-end circuit includes also a digital part, which consists of a current mode

amplitude discriminator, a voltage mode peak discriminator and a logic circuit for reset and

pulse pile up rejection. The front-end circuit includes also other features: the possibility to



disable the preamplifier and/or the discriminators in order to inhibit the channel in case of

anomalous behaviour. The power consumption of the digital section is 200 μW.

Figure 2.3-2: Block diagram of the acquisition chain

The front-end section of the ROIC performs the conventional processing of the detector signal:

low-noise amplification, pulse shaping, peak-stretching and peak discrimination, providing at the

output a dc voltage proportional to the energy of the event and a trigger signal. By contrast, the

back-end section of the ROIC performs three main functions:

• processing the trigger signals produced by the front-end section in order to provide to the

external acquisition system the information that some events have been detected;

• performing the A/D conversion of the analog data produced by the front-end section;

• delivering outside the chip the digital converted energy value and the location of the

occurred events.

When a front-end cell asserts its trigger output, the back-end section schedules the analog output

of the pixel for conversion on the A/D converter. At the same time, it starts the handshake with

the external acquisition system to deliver the converted data. Until the acquisition system

acknowledge, the ROIC continues to acquire events from the detector and stores them on the

analog output memory of each pixel. Two different modes of operation are foreseen, a slow

event-rate mode and a fast event-rate mode. In slow event-rate mode the acquisition of few



events is inhibited by disabling the front-end electronics during A/D conversion, while in fast

event-rate mode the acquisition continues during A/D conversion. Upon acknowledgement by

the acquisition system, the ROIC starts to convert and transfer the data (in slow event-rate mode

the acquisition is stopped at this point), [12].

2.3.2 The digital processing chain

The digital processing chain is a fundamental part of the chip in order to ensure the correct

operation of the read-out system. The digital chain has to enable and provide proper timing for

the A/D conversion and set the programmable parameters of the blocks of the analog acquisition

chain. The basic block diagram of the system is shown in Figure 2.3-3.

AnalogChain

Channel

AnalogChain

Channel

Row/ColumnSelector

AnalogChain

Channel

Row/ColumnSelector

AnalogChain

Channel

Row/ColumnSelector

Row/ColumnSelector

Column Selector

A/DConverter

ControlLogic

Management

Output

Trigger

Trigger

Figure 2.3-3: Digital process chain block diagram

For implementing the ROIC two different solutions have been followed:



• to use a single fast converter able to work at a frequency higher than the detected event

rate;

• to use a slower converter which can be employed in more channels, to fulfil the desired

maximum event frequency.

A pipeline converter without clock and a Wilkinson converter, typically used for the sensor

arrays, are the architectures chosen for the converters. The two converters work on two 8x16

matrix.

2.3.3 The pipeline converter

Figure 2.3-4 shows the block diagram of the system with the pipeline converter.

Figure 2.3-4 Block diagram of the system with the pipeline converter

The pipeline converter satisfies all the events that occur in the matrix with a latency time lower

than 1 µs. Thanks to the high speed in data conversion there is no need of any kind of memory.

When a photon hits the matrix, a charge signal is created. If the signal is higher than the

threshold, all circuits are enabled for the signal processing. To be more specific, the Trigger

signal is sent to the Global Trigger Logic (GTL). The GTL sends the GlobalTrigger signal to the

Acquisition Interface (AI) and freezes the state of the matrix. The AI starts the handshaking with



the external acquisition system and sends a start of conversion (SOC) signal to the A/D

converter and to the GTL. The GTL acquires the address of the pixel that has detected the event

and sen the Trigger signal and connects the output of the peak and hold circuit within the

addressed pixel to the Data line and hence to the A/D converter. After the conversion, the

converter sends an end of conversion signal (EOC) to the AI. The latter needs to deliver the

output data to the external world. When the data has been delivered, the GTL resets the peak and

hold output and the pixel address stored.

2.3.4 The Wilkinson converter

The operating frequency of the Wilkinson A/D converter is lower than the event frequency that

occurs in the matrix, but several converters are used in parallel, thus allowing all the events to be

handled in the required time.

A block diagram of the system in which is included the Wilkinson converter is shown in Figure

2.3-5.

Figure 2.3-5: Block diagram of the system with the Wilkinson converter



When a photon hits the detector, the pixel sends a signal to the Local Trigger Logic (LTL) and,

from here, to the Global Trigger Logic (GTL).

Moreover the LTL decodes the address of the pixel where the event occurs. If more events occur

in the same channel a priority logic decides the first to serve.

The GTL with the signal GlobalTrigger enables the handshaking with the external acquisition

system through the Acquisition Interface (AI). If the system is ready then the TriggerAck is sent

to the GTL, that rises the Convert signal for the LTL. The LTL addresses the pixel and gives the

Start of Conversion signal (SOC) to the channel where the event has been detected. Once the

converter has sent the End of Conversion signal (EOC) to the GTL, the results are saved and can

be fed to the acquisition system by means of the AI.

2.4 Layout of the test chip

The layout of the first test chip is depicted in Figure 2.4-1 in which the main blocks of the

system are highlighted. In particular, it is possible to note in the middle the huge matrix of

acquisition channels. The Wilkinson and the pipeline converters are located on the right and on

the bottom sides of the matrix respectively. The LTL, the GTL and the Acquisition Interface are

under the Wilkinson converter, while the memory for storing the configuration of the analog

acquisition channels (Configuration Memory) is on the left side of the matrix.

Due to the large number of connections with the back-end of the ASIC, only the first two metal

levels have been used inside each single pixel read-out cell, while the other metal levels have

been employed to ensure connections on the top level.

The clocked circuits have been arranged on the top-right corner of the ASIC and shielded with

many guard-rings. Furthermore, the routing has been kept wider than the minimum allowed from

the technology rules in order to reduce resistive paths.

The empty areas have been filled with filtering capacitors, placed between the power supply and

the ground lines. Different power pins have been placed for each block of the chip in order to

avoid any spike from the digital part to the analog one. This solution allows also monitoring the

power consumption of each block.

The microphotograph of the ROIC ASIC is shown in Figure 2.4-2. The resulting chip area is

equal to 8.6 x 7.2 mm2.



Figure 2.4-1: Layout of the first ROIC test chip

MATRIX

PIPELINE

CONVERTER

TRIGGER

LOGIC

WILKINSON

CONVERTER

CONFIGURATION

MEMORY

ACQUISITION

INTERFACE



Figure 2.4-2: Microphotograph of the ASIC ROIC



Chapter 3 The Wilkinson A/D Converter


Chapter 3

The Wilkinson A/D Converter

In this chapter we describe the Wilkinson A/D converter, and the solutions adopted to obtain the

desired specifications.

3.1 Introduction

The Wilkinson converter is a slow A/D converter able to handle a large number of acquisition

channels at the same time, thus allowing to support a large event rate in a sensor array in spite of the

long conversion time.

In ROIC test chip, described in Chapter 2, we have placed 16 Wilkinson converters, one for each

row of the 16x16 array of detectors. Each converter handles the output of the 16 pixels of the row.

In case of multiple events on the same row, the Trigger Logic implemented on the chip is able to

handle the priority of the events, [13], [39].

The main characteristics of the Wilkinson converter are:

• INL lower than ±0.5LSB;

• DNL lower than ±0.25LSB;

• Clock frequency equal to 50 MHz;

• Input range equal to 1.2 V;

• Resolution larger than 9 bit.

The block diagram of the Wilkinson A/D converter (ADC) are shown in Figure 3.1-1. The converter

consists of an input buffer, a voltage comparator, an output register, a ramp generator, a counter and

a clock generator. The ADC performs a voltage-to-time conversion of the input signal by using the

voltage ramp as reference to be compared with the output of the Peak and Hold circuit present in the

acquisition chain associated with each pixel of the matrix



Figure 3.1-1: Wilkinson A/D converter block diagram

During the conversion process, the voltage comparator compares the output of the ramp generator

with the input signal, which is a constant voltage proportional to the incident photon energy. If the

Start Of Conversion (SOC) is at the high logic level, the value of the output register is stored when

the ramp becomes higher than the input signal. The value of the register represents the digital

conversion of the signal itself. When the counter reaches the last value, it is reset to the initial value.

The clock is provided as a differential sinusoidal waveform whose amplitude is 300 mV, centred

around 1.5 V with a frequency equal to 50 MHz. This solution has been adopted in order to avoid

any noise problems due to undesired glitches related to the rising edge of the clock signal.

Moreover, there is the possibility to program the slope of the ramp signal with a resolution of 7 bit

by means of the configuration memory.

The A/D converter achieves a complete conversion in a time ranging from 20 µs to 40 µs depending

if the conversion starts at the beginning of the ramp period or at the end. Indeed, two ramp periods

are actually used for the conversion in order to avoid spurious zero-crossings of the comparator due

to the settling of the input signal (i.e. the zero-crossing with the second ramp period after SOC is

used for the actual conversion), [14], [15].

An example of the conversion cycle is shown in Figure 3.1-2.



Figure 3.1-2: Example of Wilkinson conversion

It is possible to see that the SOC signal occurs when the voltage Ramp is rising. When the Ramp

becomes larger than the input signal, the comparator output changes state. The EOC signal and the

ouput digital signal are produced at the end of the second period of the Ramp signal after SOC, as

expected.

3.2 The basic blocks

The ADC makes a voltage-to-time conversion of the input signal and exploits a voltage ramp signal

as reference to be compared with the input signal. The basic blocks of the Wilkinson converter are:

an input buffer, a comparator, an output register, a ramp generator, a counter and a clock generator.

3.2.1 Ramp generator

A block diagram of the ramp generator is show in Figure 3.1-1. A constant current loads a capacitor

thus generating the voltage ramp signal. In order to improve the linearity characteristics of the

output signal, a capacitor has been included in the feedback loop of an high gain amplifier. The



latter supplies all the current required by the following stage. In order to ensure a constant slope of

the ramp in the useful input range of the converter, the swing of the ramp signal has been extended

from 800 mV to 2.15 V, but only the central part of this range is actually used. In this way, it is

possible to neglect the second order effects and the glitches during the reset phase of the capacitor.

The value of the input current is programmed by a 7-bit DAC, controlled from the configuration

memory of the chip. The DAC has been implemented with cascode current mirrors in order to

guarantee the maximum output resistance and linearity. In order to obtain a slope of 60 V/μs, i.e.

required value to ensure the proper operation of the converter, the output current of the DAC has to

be equal to 1 μA, considering a capacitor of 18 pF.

Slope

Ib

C

-

+

Vout

CurrentDAC

Figure 3.2-1: Block diagram of the ramp generator

The overall useful output swing is 1.2 V (from 0.9 V to 2.1 V). The operational amplifier used is

based on a folded cascode architecture, as in the pipeline converter described in Chapter 4. The gain

of the amplifier is 83 dB with a GBW equal to 100 MHz.

3.2.2 Voltage Comparator

The schematic diagram of the voltage comparator used in the Wilkinson A/D converter is shown in

Figure 3.2-2. It is similar to a mirrored operational amplifier, but we added transistors M10 and M11

to obtain a positive feedback loop, which boosts the response time. These two transistors are also

used to introduce a slight hysteresis.



M1 M2

M3 M4

M5

M6

M7

M8

M9

M11M10

MB

IBIAS

IN - IN + Vout

VDD

GND

Figure 3.2-2: Comparator schematic diagram

The output current of the comparator is given by

6

62

4

4

out mM in

WlI g V

Wl

⎛ ⎞⎜ ⎟⎝ ⎠= • •

⎛ ⎞⎜ ⎟⎝ ⎠

(3.1)

where W, l, gm are the width, the length and the tranconductance of the transistors, respectively. The

voltage gain turns out to be

( )6

62 7 6

4

4

//outout out mM ds ds

in

WlV R I g r rV W

l

⎛ ⎞⎜ ⎟⎝ ⎠= • = • •

⎛ ⎞⎜ ⎟⎝ ⎠ (3.2)

where r is is the drain to source resistance.

When applying the threshold voltage VG1 at the gate terminal of M1, and Vin at the gate of M2, with

Vin < VG1, M1 is on while M2 is off. In these conditions, M3 and M10 are turned on, while M4 and

M11 are off.



All the current i5 flows through M1 and M3, and the drain voltage of M4 is close to Vdd, thus forcing

Vout to be close to ground. When Vin overcomes the threshold voltage, i5 begins to flow through M2

until it becomes equal to the current flowing into M10. At this point, the comparator output changes.

Most of the current flows into M2 and M4, thus causing M11 to be turned on and M3 and M10 off.

When Vin decreases and becomes lower than the threshold voltage, i5 begins to flow through M1.

The comparator output changes when i1 equals i11 (the current flowing through M11).

It is worth to point out that the hysteresis is controlled by ratios β10/β3 and β11/β4, related to

transistors M10-M3 and M11-M4 respectively. These ratios have to be larger than 1.

If VREF is applied at the gate terminal of M1 and the ratios β10/β3 and β11/β4 have the same value,

hysteresis turns out to be centred at VREF as shown in Figure 3.2-3.

To enhance the circuit performances, an output stage (i.e., transistors M6 and M7) and two inverters

have been added, thus obtaining sharp output transitions.

The transistors sizes are summarized in Table 3.2-1:

VREF V+V-

Vout

Vin

Figure 3.2-3: Comparator transfer function with hysteresis

Transistor W/L (μm/μm)

M1, M2, M3, M4 35/0.35

MB, M6, M7, M8, M9 40/0.35

M5 50/0.35

M10, M11 100/0.35

Table 3.2-1: MOS transistors sizes in the comparator



3.2.3 The clock generator

The comparator structure discussed in the previous section has been used also in the clock

generator, by using as input signal a 300-mV sinusoidal differential waveform. The comparator

transforms this sinusoidal differential clock into a single-ended square wave, which is then used

throughout the chip.

To increase the speed of the comparator, the bias current has been slightly increased. This is not a

critical issue in the overall power budget, because the clock is common for all channels. Its power

consumption must be, hence, divided among all the used cells.

3.2.4 The counter and the output register

The counter has been designed to obtain a Gray code at the output. In this way, considering that the

comparator can change its state at any time independently of the clock signal, if we take the output

of the register before the settling transient after the clock rising edge is finished, we have an error

that is lower then 1LSB. The counter has been designed by using an automatic tool and then, with a

place and route program, inserted in the layout.

To implement the output register, ten flip-flops controlled with the comparator output as clock

signal have been adopted. At the rising edge of the comparator output, the register copies the output

of the counter. The Gray-to-binary conversion is performed at the output of the counter.

3.3 Measurement Setup

The measurements on the prototype chip have been carried out at the Alcatel Alenia Space LABEN

laboratories where a complete customized setup has been built.

The setup, shown in Figure 3.3-1, consists of standard instruments, like power supply, voltage

generators, voltmeters and oscilloscope. The ROIC ASIC, which includes the Wilkinson ADC, has

been hosted in a ad-hoc board with flash based FPGA for the readout procedure. To obtain the

digital acquisition we used a PC which implements some automatic procedures. Moreover, we have

used a National Instruments Acquisition Board and a MIO PCI Configuration Board.



Figure 3.3-1: The measurements setup

A screen-shot of the acquisition tool is shown in Figure 3.3-2.



Figure 3.3-2: Automatic acquisition tool developed at Alcatel Alenia Space LABEN laboratories

3.4 Experimental results

The chip has been measured to verify its correct operation. First of all, we verified the functionality

of the system with a single input signal. Figure 3.4-1 shows the measured waveforms obtained

during the Wilkinson ADC conversion cycle. It is possible to note the Start of Conversion signal

(SOC, channel 4) in the middle of the voltage ramp period, the ramp signal itself (channel 1) and the

End of Conversion (EOC, channel 3).



Figure 3.4-1: Waveforms of the Wilkinson ADC conversion cycle

To verify the linearity of the ADC in the acquisition chain, we applied an input signal directly to the

converter through a dedicated multiplexer implemented on-chip.



Figure 3.4-2: DNL (a) and INL (b) of the Wilkinson A/D converter

The DNL and INL of the A/D converter, depicted in Figure 3.4-2, are of the order of ±0.5LSB and

±0.6LSB respectively with a resolution of 10 bit.

Figure 3.4-3:DNL (a) and INL (b) of the complete acquisition chain

b)a)

a)

b)



To verify the DNL and the INL of the entire acquisition chain, a pulse generator was used to

simulate the signal from the detector, with a rise time of 100 ns, duration 50 µs, fall time of 200 µs,

a pulse rate of 500 Hz and with amplitude varying continuously (ramp mode) between 50 mV and

1795 mV and a duration set to 900 s. The equivalent charge range is deemed about 300 to 11200

electrons.

From this measurement the DNL of the end-to-end chain is in the order of ±0.2LSB, and the INL in

the order of ±3.3LSB, as shown in Fig. 3.4-3.

Figure 3.4-4: Spectrum of the Wilkinson chain

Figure 3.4-4 shows an energy spectrum obtained by applying different input signals to the whole

chain. We considered 10 peaks with amplitudes ranging from the minimum detectable signal to near

the maximum ADC code, which correspond to a charge signal varying from 144 e- to 10300 e-. The

width of the peaks leads to an input referred noise as low as 34 e-.

The offset and the gain of the chains have been also measured. The output of the peak and hold has

been evaluated both with the internal ADC and with an external multichannel analyzer (MCA), as

shown in Figure 3.4-5 and Figure 3.4-6.



Figure 3.4-5: Offset of the Wilkinson converters.

It is possible to notice the correlation between the two data set: the variation in the offset levels at

the output of the peak and hold is directly reflected into the ADC output code.

Also the gain of the various channels with the ADC has been evaluated to understand if there was

any spread due to the process or not. The correlation suggests that the ADC gain variation between

channels has a negligible effect with request to the gain variation within the front-end electronics.

Figure 3.4-6: Gain of the Wilkinson converter



The power consumption of the whole chain is 0.9 mW while the power consumption of one A/D

converter stand alone is about 0.4 mW.

3.5 Conclusions

In this chapter we have presented a Wilkinson A/D converter able to resolve 10 bit. The main

characteristic of the converter are:

• INL lower than ±0.5 LSB;

• DNL lower than ±0.6 LSB;

• Clock frequency 50 MHz;

• Input range 1.2 V.

The basic blocks of the Wilkinson converter are: an input buffer, a comparator, an output register, a

ramp generator, a counter and a clock generator.

For every row of channels of the pixel matrix there is one Wilkinson A/D converter. Therefore, each

Wilkinson converter serves sixteen pixels and its power consumption is averaged, thus allowing to

maintain the overall power consumption lower than 1 mW per channel.

The A/D converter has been measured to evaluate its performance both as stand-alone cell and

inside the acquisition chain. The DNL and INL obtained considering the whole acquisition chain are

lower than ±0.2LSB and ±3.3LSB, respectively with a resolution equal to 10 bits.

The offset and gain spread between the different channels is not substantial. The input referred

noise achieved in the whole chain is as low as 34 e-.



Chapter 4 Clock-less Pipeline-Like A/D Converter


Chapter 4

Clock-less Pipeline-Like A/D Converter

In this chapter the pipeline-like A/D converter structure is presented and discussed. The peculiarity

of this converter is the absence of the clock signal. Indeed, it acts as a combinatory logic, thus

avoiding the presence of a synchronization signal for all stages.

4.1 Introduction

The pipeline-like A/D converter realized is a fast A/D converter without clock, able to convert all

the data arriving from the pixel matrix with a latency time lower than 1 μs. Peculiarity of this

converter is the absence of a clock signal, that leads a significant reduction of the noise introduced

by the synchronization signal (clock) typical in the traditional high-speed A/D converter

implementations. The absence of the clock introduces the disadvantage of the lack of a signal that

indicates the end of the conversion. To be able to synchronize the output data, therefore we realized

an analog delay cell that emulates the latency of the converter, [16].

This cell is excited by the Start of Conversion (SOC) signal and generates an End of Conversion

(EOC). The delay introduced has to ensure the complete conversion of the input data also in the

worst process corners. The idea to realize a converter without clock arises from the ascertainment

that a traditional pipeline A/D converter for static signals acts as a sequential machine. The

innovative idea is in changing the approach to the conversion systems, passing from a sequential

operation of several cells to a combinatorial operation of the circuit. Actually, the data at the input

enters into the chain which evolves until it reaches a steady state, provided that the input signal

remains constant (this is the case in the ROIC chip since the input signal to the A/D converter is

provided by a peak and hold circuit). The block diagram of the ADC is shown in Figure 4.1-1. The

output of the first stage evolves with the time dictated mainly from the residue amplifier. The

second stage begins to work on the output of the first stage when it is not yet stable and, therefore,

the time required to reach the steady state turns out to be smaller than in a traditional

implementation with clock. Moreover the successive stages do not have to wait for the end of the

operation of the previous stages and can begin their conversion, limiting the dead-times required in



a traditional architecture to accommodate the worst cases. When the steady state is reached, the

digital output is the exact conversion of the input data, [17], [18].

For this A/D converter we have used 9 stages which resolve 1.5 bit for stage, with a final 2 bit flash

stage. The error correction logic is needed for the recombination of the bits, as is possible to see in

Figure 4.1-1.

The GlobTrigD signal is needed to synchronize the data with the Trigger Logic.

Figure 4.1-1: Block diagram of the pipeline converter

The choice to use 1.5 bit per stage with digital correction logic allows us to tolerate a larger offset

in the comparators used in each stage, [19].

4.1.1 Error on the gain of the amplifier

Let us consider the case of a non ideal 1.5 bit cell, with an error in the amplifier gain, [20]. We

have:

( )2 1 AA E= −i (4.1)

where EA is the gain error . The cell transfer characteristic is shown in Figure 4.1-2



Figure 4.1-2: 1.5 bit cell transfer characteristic

Considering a signal applied at the input of the stage equal to

4ref

inVV V= − Δ (4.2)

where ΔV is small compared to Vref/4, the digital output is D = 01 and the DAC output is VDAC = 0.

At the output of the amplifier holds:

( ) ( )2 14 4ref ref

out in DAC AV VV A V V A V A E⎛ ⎞= − = − Δ = −⎜ ⎟

⎝ ⎠i i i i (4.3)

where ΔV has been neglected.

The amplitude error is EAVref/2. In the worst case, if the ADC threshold has an error equal to -Vref/4,

the error turns out to be EAVref.

If n’ is the resolution left after the cell, to obtain an error lower than x LSB the following has to be

satisfied

'2 2nA ref refE V x V−<i i i (4.4)

The gain error leads to a shift in the level and then a loss in the output code. To obtain an INL

between ±½LSB, it is necessary to have x= ½ , thus obtaining:

'2 nAE −< (4.5)

If we have 10 bit of resolution, for the first cell we have n’=9. Than the maximum error is:

9 32 2 10AE −< − • (4.6)



4.1.2 Error on the DAC level

If we consider an offset ΔVDAC on the ideal VDAC, the error at the output of the amplifier turns out to

be equal to 2ΔVDAC. By following a mathematical approach as the one discussed in the previous

section, we obtain

'2n

DAC refV x VΔ < i i (4.7)

With A = 2 (we have supposed no error in the amplifier gain) and x = ½, we have

9 22 2 2

refDAC

V LSBV +Δ < =i (4.8)

Since the difference between VDAC and Vin, directly affects the input of the cell, each error on the

DAC level in the first stage will affect the output of the second. Since for every stage the error is

amplified by a factor of two, it is mandatory that the error on the first cell is compatible with the

resolution of the complete converter. This means also that for each stage following the first one the

requirements about the maximum error tolerated double respect with the previous stage.

4.1.3 Error in the comparator threshold

In order to understand the effect of an error on the comparator threshold, we can look at the ideal 1

bit per stage cell and consider an input swing equal to 1 V, obtaining the situation in Figure 4.1-3,

[21].



Figure 4.1-3: 1 bit cell stage operation

In this case, the only threshold that the comparator uses is equal to 0.5 V and the output level of the

DAC is VDAC = 0 V, if the input of the ADC is lower than 0.5 V, while we have VDAC = 0.5 V in the

other case. Figure 4.1-4 explains the converter behaviour when an error on the threshold is present.



Figure 4.1-4: 1 bit cell operation with error on the threshold

Due to the error on the threshold we have a wrong conversion of the input data and a negative

residue Vres, equal to:

( )2 0.4 0.5 0.2ResV = − = −i ( 4.9)

The resulting residue is out of the input swing for the next stage. The next stage will give a negative

output again, since there is no possibility to recover from this error (Vres2=2(-0.2-0.5)=-1,4 V).

Instead, if we use a 1.5 bit per stage architecture, we have three decision zone, thanks to the two

comparators and we are able to recover from this problem using a suitable logic to recombine the

bits produced by the different stages. Basically, we are adding redundancy, which is useful to

recover from the error. The output of the residue amplifier that has an maximum amplitude lower

than in the 1 bit case and, therefore, the amplifier avoids saturation even with an error in the

threshold level. Figure 4.1-5 shows the ideal operation of a 6 bit A/D converter with 1.5 bits per

stage, while Figure 4.1-6 illustrates the case in which a threshold error is present.



Figure 4.1-5 : Ideal 6 bit A/D converter output with 1.5 bits per stage

0010 00 01 00 01

00 0 0 11

0.5V

-0.125V

0V

-0.5V

-0.125V

-0.4V

-0.3V

-0.1V

-0.2V

-0.1V

Figure 4.1-6: 6 bit A/D converter output with 1.5 bits per stage and offset on a threshold

From Figure 4.1-6 we can notice that the converter can recover at the fourth stage from the initial

error.

In order to obtain the proper digital code at the output from the bits produced by the different

stages, we need an error correction logic, which basically adds the different bits according to Table

4.1-1.



b17 b16 +

b15 b14 +

b13 b12 +

b11 b10 +

b09 b08 +

b07 b06 +

b05 b04 +

b03 b02 +

b01 b00 =

O09 O08 O07 O06 O05 O04 O03 O02 O01 O00

Table 4.1-1: The error correction logic function characteristic

4.2 Basic blocks

The A/D converter consists of 9 1.5 bit stages and a final 2 bit flash stage, [22].

The 1.5 bit cells are composed by a flash ADC with 3 levels, a DAC and an amplifier. The analytic

expression of the amplifier output is

( )2out in DACV V V= −i (4.10)

which is graphically reported in Figure 4.2-1.



Figure 4.2-1: Transfer characteristic 1.5 bit cell

To obtain a correct conversion, the first stage needs to have a residue with a precision higher than

the minimum signal detected by the converter. This is possible if the amplifier DC gain in the open

loop configuration is equal to 2KA = , where k is the resolution of the converter.

4.2.1 The ADC

Two types of ADC have been implemented in the pipeline converter, one used in the 1.5 bit cell and

one employed in the final flash stage. They are depicted in

-Vref 1.35 V

+Vref 1.65 V

Figure 4.2-2 and

-Vref 1.2 V

Vagnd 1.5 V

+Vref 1.8 V

Figure 4.2-3, respectively.



-

+VLSB

Vin

-VRef

-

+VMSB+VRef

-Vref 1.35 V

+Vref 1.65 V

Figure 4.2-2: 1.5 bit ADC.

-

+

Vb2

Vin

-VRef

Vb1+Vagnd -

+

-

+

+VRef

-Vref 1.2 V

Vagnd 1.5 V

+Vref 1.8 V

Figure 4.2-3: 2 bits ADC

The comparator scheme is the same used in the Wilkinson A/D converter, which has been discussed

in Chapter 3. Also in this case an hysteresis has been introduced in the voltage comparator in order

to avoid oscillations due to noise when the input signal is close to the threshold. Even if this can be

considered as an offset added to the threshold, the error correction logic can compensate for the

introduced error.



4.2.2 The residue amplifier

The residue amplifier has been realized by employing an operational amplifier with resistive

feedback, as shown in Figure 4.2-4.

-

+

VoutVin

VDAC

R2

R1

Figure 4.2-4 The residue amplifier

The output voltage of this circuit is equal to:

2 2

1 1

1 2out in DACR RV V VR R

⎛ ⎞= + −⎜ ⎟

⎝ ⎠i i i

(4.11)

To obtain the right expression of the transfer function, R2 must be equal to R1 and VDAC has to be

twice the ideal value. In particular, R1 and R2 have been chosen equal to 100 kΩ.

A folded cascode architecture followed by a source follower ouptut stage has been adopted for the

operational amplifier. With this approach the required voltage gain is obtained by the first stage,

while the resistive load can be driven by the output stage. A PMOS input stage has been adopted in

order to cancel out the body effect contribution by connecting the bulk terminal with the source.

The schematic of the amplifier is shown in Figure 4.2-5.

Simulation results show a voltage gain, a gain bandwidth product and a phase margin equal to 83

dB, 100 MHz and 50°, respectively, as shown in Figure 4.2-6. These values allow obtaining a 10-bit

conversion in about 200 ns.



Figure 4.2-5: Schematic of the operational amplifier used in the residue amplifier

The components sizes are summarized in Table 4.2-1.

Device Value

M1, M2 100/0.5 µm/µm

M3, M4 90/0.5 µm/µm

M5, M6 150/1 µm/µm

M7, M8 100/0.35 µm/µm

M9, M10 250/2 µm/µm

M11 300/0.35 µm/µm

M12 200/0.35 µm/µm

M13 260/0.35 µm/µm



M14 90/0.35 µm/µm

M15 250/0.35 µm/µm

M16 2/0.35 µm/µm

Mb 450/0.35 µm/µm

R 3 kΩ

C 5 pF

Ibias 100 uA

Table 4.2-1: Design parameters of the operational amplifier used in the residue amplifier

Figure 4.2-6: DC gain and phase margin of the amplifier in open loop configuration

4.2.3 The DAC

The DAC has to produce the proper analog signal according to the bits bo and b1 provided by the

ADC. The precision required for the conversion from the digital to the analog domain influences

the value of the residue at the output of the cell. For the first stage, this precision must be higher

than VFS/2k, where k is the number of bits resolved by the stage, and VFS is the full scale voltage.

Figure 4.2-7 shows the circuit used for the DAC.



Figure 4.2-7: Schematic of the DAC

The DAC output levels are:

V1 = 0.9 V

V2 = 2.1 V

Vagnd = 1.5 V

4.2.4 The EOC generator

The EOC generator, as mentioned above, has to delay the SOC signal. The generator consists of

two inverters, two comparator and an RC delay line, as shown in Figure 4.2-8.

Figure 4.2-8: EOC generator schematic diagram



When a positive pulse occurs at the input node s_c, at the output of the first inverter holds

-1V

t

DDV e τ−= i (4.12)

where 1 3onMC Rτ = ⋅ (RonM3 is the on resistance of the NMOS M3 transistor) so that the voltage at

this node can rapidly decrease. At the output of the second inverter holds

( )- '

2V 1t

DDV e τ−= ⋅ − (4.13)

where τ’ =C2 * R2.

When V-1 reaches 1.5 V, the input of the second comparator switches to the high logic level. When

the SOC goes down, C1 starts to charge following this equation

( )- ''

1V 1t

DDV e τ−= ⋅ − (4.14)

where τ’’=R1C1

The output of the first inverter switches to the low logic level with a delay proportional to τ’’,

causing a fast discharge of C2 through the on resistance of the NMOS transistor. The circuit is able

to delay the rising edge and the falling edge with different delay values. In particular τ’ controls the

delay of the rising edge, while τ’’ the delay of the falling edge. For a correct timing, the edges have

to occur only when the output data is ready. To ensure the correct conversion in all process corners

in which the converter has been simulated, the EOC occurs after 700 ns.

4.3 Simulation Results

Let us assume that the input signal of the i-th stage is equal to the threshold voltage of one of the

two comparators of the ADC. As in any electronic circuit some noise is superimposed to the signal.

Every time the input signal, because of the noise, crosses the threshold voltage, the comparator

output changes state, thus producing a change in the output bits of the converter stage. These bits, in

turn, produce a change in the output voltage of the DAC, thus inducing a variation of the residue.

Therefore, a noise on the input signal when it is close to the threshold voltage of the ADC induces a

variation of the residue. This residue is the input of the next stage and therefore the noise is

propagated in all the stages of the converter. Moreover, if the output bits are stored after the adder,

additional noise is produced. In fact, if the adder never has stable inputs, it produces an output (the

digital conversion of the input value) that is continuously changing. The can produce an equivalent



noise as large as the most significant bit (MSB). Simulating the A/D converter in Simulink with a

constant input equal to the high threshold of the ADC and with 1 LSB of superimposed noise, we

obtained the result shown in Figure 4.3-1, that illustrates the not corrected operation of the

structure.

Figure 4.3-1: Output of the converter with 1LSB noise on the input signal

The above drawbacks can be overcome by adopting the following solutions: introduce hysteresis in

the comparators of the ADC and store the converted bits before sending them to the adder. Different

kind of hysteresis have been simulated, either centered around the comparator threshold or shifted

towards the top or the bottom The converter has been simulated to estimate the DNL (Differential

Not Linearity) and the INL (Integral Not Linearity). In particular the DNL does not have to exceed

the value of 0.5 LSB (least significant bits).

The main simulation results are here summarized

• Hysteresis aligned at the bottom on both comparators

Passing from 200 mV to 210 mV of hysteresis the DNL becomes higher than 0.5 LSB.

• Hysteresis aligned at the top on both comparators




• Hysteresis aligned at the bottom on the high threshold comparator and hysteresis aligned at

the top on the low threshold comparator


• Hysteresis centered on both comparators


As it can be noticed, all the cases respect the 1.5-bit per stage theory of a A/D pipeline converter:

the digital correction technique corrects threshold errors up to VREF/4 (VREF = 0.6 V). The final

choice was to use a centered hysteresis around the threshold voltage level, because of its layout

symmetry. The amplitude of the hysteresis is set to 100 mV because it ensures a good robustness to

noise and at the same time it is lower than VREF/4 and hence it does not affect the linearity of the

conversion, obtaining in the same condition of 1 LSB noise over an input signal near the high

threshold of the DAC, the results shown in Figure 4.3-2.

Figure 4.3-2 : Output code of the 1LSB input noise at the converter with the hysteresis solution

The maximum error that we can have is one LSB on the final value, as expected with 1 LSB noise.



Figure 4.3-3 : Converter structure with the Flip Flop Array

Figure 4.3-3 shows the modified structure of the converter. It is possible to see that the conversion

bits are sampled from the array of flip-flops controlled by the EOC signal before the error

correction logic. Subsequently the data are sent to the Ripple Carry Adder that carries out the sum

producing the 10 bits of conversion. The time required for the addition is equal to 9 tcarry where tcarry

is required for a full adder to produce the carry output. Therefore after approximately 9 tcarry from

the rising edge of the EOC signal we have the 10 bit word that constitutes the conversion of the

analog value at the input.

The maximum error that we obtain also in this case with 1 LSB noise superimposed to the signal is

one LSB, as shown in Figure 4.3-4.



Figure 4.3-4: Output code of the converter with 1 LSB noise superimposed to the input signal with the flip-flop

array

The simulated DNL and INL of the converter are shown in Figure 4.3-5 and Figure 4.3-6.



Figure 4.3-5: DNL of the converter

Figure 4.3-6: INL of the converter



The converter has been simulated also in the process corners, as shown in Table 4.3-1, where tm=

typical case, ws=worst speed, wz=worst zero, wo=worst one, wp=worst power, achiving results

within specifications.

Corner Model transistor CMOS

Model resistance

Model capacitor Temperature

Corner 1 tm tm tm 27° C

Corner 2 tm tm tm 80° C

Corner 3 tm tm tm -20° C

Corner 4 wp wp wp 27° C

Corner 5 wp wp wp -20° C

Corner 6 ws ws ws 27° C

Corner 7 wo wp wp 27° C

Corner 8 wz wp wp 27° C

Corner 9 wo ws ws 27° C

Corner 10 wz ws ws 27° C

Corner 11 ws ws ws 80° C

Table 4.3-1: Process Corners


A test chip of the pipeline-like A/D converter has been fabricated in a 0.35 µm CMOS and has been

measured to evaluate either static (INL & DNL) and dynamic (SNR & ENOB) performance

parameters. An external sample and hold was used to match the configuration of the final

spectrometry application.

Figure 4.4-1 and Figure 4.4-2 show the measured DNL (within ±1.2LSB) and INL (within ±2.5

LSB), respectively.



Figure 4.4-1: DNL of the converter

Figure 4.4-2: INL of the converter



Figure 4.4-3: Spectrum of the converter

A typical FFT spectrum measured with an 80 kHz input sine wave at 400kS/s is reported in Figure

4.4-3. Notice that operating with a ratio fin/Fs=1/5 corresponds to have large steps between

consecutive samples, and this could be critical for a clock-less ADC. In addition an external sample

and hold is not perfectly suitable for this timing-less converter. However, in this case a SNR=55.4

dB (i.e. an ENOB = 8.91b) is achieved.

Figure 4.4-4 shows the microphotograph of the test chip, whose size is 2.4 mm2.

Figure 4.4-4: Microphotograph of the pipeline-like A/D converter

The values obtained for the INL and DNL and the conversion time obtained make this converter not

completely suitable for the desired application. A possible solution is to increase the current in

order to fulfil the speed requirements, thus worsening the power consumption of the converter.



Anyhow, for the implementation of the final ROIC chip, we decided to use the Wilkinson A/D

converter described in Chapter 3.

4.5 Conclusion

A 10 bit pipeline-like A/D converter without clock for X-ray spectrometry is presented. The

converter is able to convert an input signal in 2.5 μs and achieves almost 9 bits of effective

resolution. The structure consist of 9 1.5 bit stages and a final 2 bit flash stage. The structure acts

like a combinatory logic, propagating the residue asynchronously along the stages. An EOC

generator is needed to indicate when the conversion is finished.

The overall features of the converter are summarized in the following outline:

• Power consumption 95 mW (12 [email protected] )

• DNL ± 1.2 LSB

• INL ± 2.5 LSB

• SNR 55.4 dB

• Conversion time 2.50 μs



Chapter 5 Magnetic Sensor Model


Chapter 5

Magnetic Sensor Models

In this chapter different kinds of magnetic sensors will be discussed together with the possibility to

create a model that can help the designer during the development of an integrated sensor system.

The attention will be focused on four types of magnetic sensors: hall devices, magneto-resistance,

flux-gate and magneto-inductance.

The models will be compared with a real sensor to validate the model itself.

5.1 Introduction

Magnetic sensors have been used since hundreds of years. At the beginnings they were available

only as mechanical devices for navigation and orientation in open spaces. Recently to detect a

magnetic field it is possible to use both mechanical and electronic sensors. The main advantage of

the electronic sensors, which have been recently developed, is that they can be integrated together

with electronic interface circuits in the data processing flow. There are several types of magnetic

sensors, but, basically, all of them, when detecting a magnetic field, show a small variation of a

physical property or parameter of the device. The entity of this variation, which is related to the

sensitivity of the sensor to the magnetic field applied, makes the sensor itself suitable for a specific

type of application. It is thus possible to classify the magnetic sensors by using their field sensing

range. As shown in Table 5.1, three categories can be identified: low field, medium field, and high

field sensors. Magnetic fields less than 1 μgauss (where 1 gauss = 100 μT) are very low and well

below the Earth magnetic field. Sensors with a range of 0.1 gauss to few gauss will be considered

Earth’s field sensors and sensors with sensitivity above 10 gauss can be considered bias magnet

field sensors. For measuring the Earth magnetic field with devices that are suitable for portable

applications, the magneto-resistance and magneto-inductance (to be used as discrete sensors) are

available as well as the Fluxgate magnetic sensors. Fluxgates and magneto-resistances require using

magnetic material. They, as well as magneto-transistor and Hall sensors, can be integrated by using

CMOS technologies. It has to be pointed out that the use of magnetic material as concentrator can

help in increasing their sensitivity. Fluxgate, Hall sensor with magnetic concentrator, magneto-



transistors allow achieving 2D on chip,[23]. By contrast, conventional Hall devices are for 1D.

Each sensor has specific characteristics that make it suitable for a given range of applications. In

addition to the sensitivity, it is necessary to take into account the range of temperature, the sensor

volume, and its on-chip manufacturability.

Table 5.1: Classification of magnetic sensors

Nowadays there are several different applications where magnetic sensors can be used. Among

them electronic compasses, sensors for traffic control, magnet activated switches for cellular

phones, and notebooks or handheld devices can be mentioned. Other applications are in the

automotive field or home appliances: devices based on magnetic sensors are used, for example, to

control the car engine or apparatus used in domestic ambient.[24]

In order to be able to design circuits for a particular application, which requires also magnetic

sensors, it is thus important to have tools able to properly describe the behaviour of magnetic

sensors.

5.2 Types of magnetic sensors

As shown in Table 5.1, there are several magnetic sensors that use different technologies to detect

magnetic field. The principles of operation of the most used types of magnetic sensors are listed

below.



5.2.1 Squid

The magnetic sensor with the highest sensitivity is the Superconducting Quantum Interface Device

(SQUID). Developed around 1962, it is able to detect magnetic fields from few fempto-Tesla to

tens of Tesla. It is used in medical applications since it can detect the human brain neuro-magnetic

field (about few femptoTesla). The main drawback of such a sensor is the low temperature of

operation (about 4 K) needed to cool down the junction required to measure the current induced by

the magnetic field.

5.2.2 Search-coil

Search coils are based on the induction Faraday law, that establishes that the induced voltage in a

coil is proportional to the variation of the magnetic field concatenated to the same coil. This voltage

creates a current that is proportional to the speed of the variation of the field itself. The sensitivity

of the search-coil depends on the properties of the magnetic material used, the area of the coils and

the number of coils used. The direct application of the Faraday law makes this sensor not suitable

for static or slow frequency fields.

5.2.3 Magneto-inductive sensor

The magneto-inductive sensor is a new type of magnetic sensor developed about fifteen years ago.

Nowadays it is one of the cheapest and most used sensor thanks to its reliability. For this reason, a

model of such a sensor has been developed and will be discussed in this chapter. The magneto-

inductive sensor is basically a solenoid with magnetic material inside. If a current flows inside the

solenoid, it generates a magnetic field and an induced voltage. By linking this voltage to the initial

current it is possible to obtain the value of the inductance of the sensor. An external magnetic field

Hext changes the value of the magneto-inductance, since it changes the value of the induced voltage

by the sensed magnetic field. By employing a circuit able to detect the value of the inductance, it is

possible to derive the value of an external magnetic field.



5.2.4 Magneto-resistance

Magneto-resistive sensors are based on the anisotropic magneto-resistance effect (AMR) and have

been developed in the last 30 years, [25]. Magneto-resistive sensors exploit the fact that external

fields H influence the electrical resistance ρ of certain ferromagnetic alloys. This solid-state

magneto-resistive effect can be easily realized by using a thin film technology. The specific

resistance ρ of anisotropic ferromagnetic metals depends on the angle θ between the internal

magnetization M and the current I, according to

( ) ( ) ( )2|| cosp pρ θ ρ ρ ρ θ= + − (5.1)

where pρ and ||ρ are the resistivities perpendicular and parallel to M, [26]. The quotient

( )|| / /pρ ρ ρ ρ ρ− = Δ (5.2)

is called the magneto-resistive effect and may amount to several percent. Sensors are always made

of ferromagnetic thin films as this has two major advantages over bulk material: the resistance is

high and the anisotropy can be made uni-axial. The ferromagnetic layer behaves like a single

domain and has one distinguished direction of magnetization in its plane called the easy axis (e.a.),

which is the direction of magnetization without external field influence.

Figure 5.2-1: Geometry of a simple sensor

Figure 5.2-1 shows the geometry of a simple sensor where the thickness (t) is much smaller than the

width (W), which, in turn, is less than the length (L) (i.e. t « W ‹ L). With the current (I) flowing in



the x-direction (i.e. θ = 0 or Θ = φ) the following equation can be obtained from (1)

( )20 cosR R R ϕ= + Δ (5.3)

and with a constant current I, the voltage drop in the x-direction Ux becomes

x pU Iρ= (5.4)

Besides this voltage, which is directly linked to the resistance variation, there is a voltage in the y-

direction, Uy, given by

y pU Iρ=

This is called the planar or pseudo Hall effect. It resembles the normal or transverse Hall effect but

has a physically different origin. The signal obtained from the sensor depends on the angle between

the magnetization M and the x-direction. If M changes due to external field H, this field determines

the sensor signal.

It is possible to show that

2

0

1 1 yx p

HLU IW t H

ρρρ

⎛ ⎞⎛ ⎞⎛ ⎞⎛ ⎞Δ⎛ ⎞ ⎜ ⎟⎜ ⎟= • • + • − ⎜ ⎟⎜ ⎟ ⎜ ⎟ ⎜ ⎟⎜ ⎟•⎝ ⎠ ⎝ ⎠ ⎝ ⎠⎝ ⎠⎝ ⎠ (5.5)

( )

1/ 22

0 0

/ 1y yy p

H HU I L t

H Hρρρ

⎛ ⎞⎛ ⎞Δ ⎜ ⎟= • • • • − ⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠ (5.6)

In the case in which Hx ≠ 0, it is necessary to replace H0 with H0+ Hx/cos φ.

The main difference is that Ux depends on the square of Hy/H0 while the Hall voltage Uy is linear

for Hy<<H0, and the ratio of their maximum values is L/W, so that the Hall voltage is lower

(L>>w).

As shown in Eq. 5.5, the basic magneto-resistor has a square resistance-field (R-H) dependence, so

a simple magneto-resistive element cannot be used directly for linear field measurements. A



magnetic biasing field can be used to solve this problem. A better solution is linearization using

barber-poles, that consists in inserting conductive elements inside the sensor to rotate the current.

This type of sensor shows a widest range of linearity, a smaller resistance and the minimum

associated distortion than any other form of linearization, and it is well suited to medium and high

fields.

Figure 5.2-2: Use of barber poles to linearize the current

The current takes the shortest route in the high-resistivity gaps which, as shown in Figure 5.2-2, is

perpendicular to the barber-poles. Barber-poles inclined in the opposite direction will result in the

opposite sign for the R-H characteristic, making it extremely simple to realize a Wheatstone bridge

set-up. The signal voltage of a Barber-pole sensor may be calculated from the basic Eq. 5.5 with Θ

= φ+ 45°

( )1/ 22

0 0

/ 1 12

y yx p

H HU I L wt

H Hρ ρρ αρ ρ

⎛ ⎞⎛ ⎞⎛ ⎞Δ Δ⎜ ⎟⎜ ⎟= • • + ± • • − ⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠⎝ ⎠ (5.7)

where α is a constant arising from the partial shorting of the resistor, amounting to 0.25 if barber-

poles and gaps have equal widths.

The characteristic, plotted in Figure 5.2-3, shows that for small values of Hy relative to H0, the R-H

dependence is linear. In fact this equation gives the same linear R-H dependence as the planar Hall-

effect sensor, but it has the magnitude of the magneto-resistive sensor. Figure 5.2-4 shows an

example of integrated magneto-resistance.



Figure 5.2-3: Effect of the barber poles (continuous line) and the normal sensor (dot line) versus magnetic field

Figure 5.2-4: An integrated magneto-resistance



5.2.5 Hall sensor

The Hall effect was discovered by Dr. Edwin Hall in 1879. Dr. Hall found that when a magnet was

placed so that its field was perpendicular to one face of a thin rectangle of gold through which

current was flowing, a difference in potential appeared at the opposite edges. He found that this

voltage was proportional to the current flowing through the conductor, and the flux density or

magnetic induction perpendicular to the conductor. When a current-carrying conductor is placed

into a magnetic field, a voltage will be generated perpendicular to both the current and the field.

This principle is known as the Hall effect. Figure 5.2-5 illustrates the basic principle of the Hall

effect. It shows a thin sheet of semiconducting material (Hall element) through which a current

flows. The output connections are perpendicular to the direction of the current. When no magnetic

field is present, the current distribution is uniform and no potential difference is seen across the

output. When a perpendicular magnetic field is present a Lorentz force is exerted on the current.

This force disturbs the current distribution, resulting in a potential difference (voltage) across the

output. This voltage is the Hall voltage (VH).



Figure 5.2-5: Effect of the magnetic field on the current

For the Lorentz’s law, a charged particle q moving inside the conductor in a magnetic field B with a

speed equal to vd, is subject to a force equal to:

dF q v B= ×i (5.8)

where × is the vectorial product operator between vd and B.

In stationary condition this force is balanced by the induced electrical field generated from a charge

redistribution, named Hall field HE. The integral of this field along the conductor gives the Hall

voltage VH. This voltage is equal to VH = EH W in the case that B is uniform along the conductor,

where W is the width. An electron placed inside the conductor is subject to a force equal to F =

qEH.

Using Eq.5.8, and using vd = -Jx/q, where Jxis the current density, it results

H dq E q v B=i i i (5.9)

that means

H H xE R J B= i i (5.10)



where RH is defined as the Hall coefficient. By considering parameter r that takes into account

statistic variation of speed of the carrier (+ for electrons or – for holes)

H

rRq

= ± (5.11)

it is possible to express the Hall voltage as

810H H

I BV Vt

=ii

(5.12)

By using this equation it is possible to determine the type of carriers and the concentration. From

this values and knowing the current, it is then possible to obtain the conductivity and the Hall

mobility (μ = σ|RH|).

5.2.6 Fluxgate

Fluxgate sensors are among most used magnetometers thanks to their possibility to be integrated

together with microelectronic circuits. Fluxgate magnetometers were first introduced in the 1930’s.

Some development was for airborne magnetic surveys and for submarine detection, like Hall

devices. They were further developed for geomagnetic studies, for mineral prospecting and for

magnetic measurements in outer space. They have also been adapted and developed for various

detections and surveillance devices, both for civil and military use. Despite the advent of newer

technologies for magnetic field measurements, Fluxgate magnetometers continue to be used

successfully in all of these areas, thanks to their reliability, relative simplicity, and economy. In the

late 1950’s, the Fluxgate was adapted to space magnetometer applications. Even as early as 1948, a

three-axis Fluxgate was used in an Aerobee sounding rocket to a peak altitude of 112 km. The first

satellite to carry a magnetometer of any type was Sputnik 3 which was launched in 1958 and carried

a servo-oriented Fluxgate. Luniks 1 and 2 (Russian lunar probes), both launched in 1958, carried

triaxial Fluxgates. The USSR Venus probe launched in 1961 carried two single-axis Fluxgates. The

first American satellite to carry a Fluxgate was Earth orbiting Explorer 6 launched in 1959. Some

satellites or space probes carrying Fluxgate have included USSR Mars probe, Nasa Explorer 12, 14



and 18, Mariner 2 (Venus) the USSR Earth-orbit Electron 2 and Apollo 12, 14, 15 and 16.

Nowadays, developments for this sensor are expected in the solution based on CMOS technology

for the coils and CMOS compatible post-process technology (i.e. sputtering) for the core deposition.

This way, it is possible to realize micro-Fluxgates featuring very low power consumption (in the

order of few mW) and minimum silicon area. They show some common point with magneto-

inductances due essentially to the common structure they have together. The basic structure of a

Fluxgate sensor, shown in Figure 5.2-6, consists of two coils: a primary (excitation) and a

secondary (sensing) coil, wrapped around a common high permeability ferromagnetic core.

Figure 5.2-6: Structure of a Fluxgate in a magnetic field

The excitation current Iexc flowing through the excitation coil produces a field that periodically

saturates the soft magnetic material core (in both directions). In saturation (Figure 5.2-6-b), the

permeability of the core drops and the DC flux associated with the DC magnetic field B0 to be

measured decreases. The name of the device derives from this “gating” of the flux that occurs when

the core is saturated. When the field to be measured is present, the 2nd harmonic, and also higher

order even harmonics, appears in the voltage Vind, induced in the sensing coil. This behaviour is

strictly related to the transfer function of the system that is the hysteresis loop of the magnetic field.

Without an external magnetic field, exciting the excitation coil with a current at frequency f, the

induced voltage will be due to the sum of different harmonics at frequency f, 3f, 5f, 7f, and so on,

according with the odd transfer function. When an external magnetic field is applied, the different

operating point degrades the symmetry in the transfer function and therefore, together with the odd



harmonics, the even harmonics will appear. The amplitude of this even harmonics, that represent

the sensor output, will be proportional to the intensity of the external magnetic field. The amplitude

of the induced voltage is described by the Faraday’s law

( )0

sin 2 excexc

ind sens

f td N I

ldV N Sdt dt

πμ⎛ ⎞⎜ ⎟

Φ ⎝ ⎠= − = −

i ii i i

i i (5.13)

where Nsens and Nexc are respectively the number of turns of the sensing coil and of the excitation

coil, S is the cross section of the sensing coil, l is the length of the excitation coil, μ is the magnetic

permeability and I0·sin(2π·fexc·t) is the sinusoidal excitation current at frequency fexc. The sensor

sensitivity can be improved by maximizing the induced voltage, and this can be done using the

following solutions:

• by increasing the excitation frequency (fexc); however, an upper bound to fexc is given by the

cut-off frequency of the ferromagnetic material relative permeability;

• by increasing the number of turns of the sensing coil (Nsens);

• by increasing the cross section of the ferromagnetic material (S), considering that a larger

cross-section requires a larger current to saturate the ferromagnetic material and, hence, an

increased power consumption.

The main drawback of Fluxgate magnetic sensors realized with the coils wrapped around the core is

the complex construction of the core and of the coils when they have to be realized within planar

technologies. For this reason, new topologies of planar integrated micro-Fluxgate were recently

presented in the open literature. For instance, a structure for a differential single axis planar

Fluxgate magnetic sensor is shown in Figure 5.2-7. The ferromagnetic core is placed over the

diagonal of the excitation coil.



Figure 5.2-7: Structure of a planar Fluxgate

Supplying the excitation coil with a suitable current, each half of the core periodically saturates in

opposite directions, as illustrated in Figure 5.2-8. When no external magnetic field is applied, the

two sensing coils, connected in antiseries, show an output voltage that ideally is zero. By contrast,

when an external magnetic field component is present and parallel to the core, the magnetization in

one half of the core is in the same direction as the external magnetic field, while the magnetization

of the other half of the core is in the opposite direction. Therefore, the voltage induced in the two

sensing coils is not the same and the differential output voltage increases its value, resulting in an

amplitude modulation. In Figure 5.2-8 three different cases are shown, when the external magnetic

field was not applied and applied with two different values.



Figure 5.2-8: Effect of the magnetic field on the sensor output

5.3 The magnetic sensor model

In order to be able to simulate the electronic interface circuits together with the magnetic sensors, a

magnetic sensor model compatible with SPICE-like simulators has to be developed. The Analog

High-level Description Language (AHDL) code developed describes the behavioural model of four

different types of sensors: a magneto-resistance, a magneto-inductance, a hall device and a

Fluxgate.



Struttura delcomponente e

comportamento(moduli)

Struttura del sistem a(netlist)Set di equazioni

Risposta del sistema

Figure 5.3-1:Structure used by simulator

In particular, the VERILOG-A language has been used

since it is suitable for the CADENCE simulation

environment, tool widely used for IC design. From a set of

equations it is possible to define the component behaviours.

This set of equation is taken by the simulator and inserted

into the netlist to obtain the behaviour of the complete

system and carry on the simulation. Of course, the more

accurate is the set of equation, the more accurate will be the

result of the simulation.

The symbol of the magnetic sensor is a 5-terminal

component. The meaning of some of the terminals can

change depending on the type of sensor. For all models two

main terminals have been introduced, one of which is the

reference, representing the electrical output of the sensor,

the other, the differential voltage across the main terminals

or the current through them is the effect of the magnetic

field. A voltage generator or a current source through a

third terminal represents the input of the device: the

external magnetic field, H. The remaining two terminals have been introduced for the pick-up coil

in Fluxgates or to monitor internal quantities for other sensors. A set of equations describes the

sensor operation. The model also includes non-idealities typical of real devices. Performance

depends on a number of parameters that are specified in the model card similarly to what is done for

Figure 5.3-2: Simulator model card



active components. The global parameters for a given type of sensor are given in the sensor model

card.

5.3.1 Magneto-resistance

The magneto-resistance changes with external magnetic field, Hext. We assume that the output

variable is the voltage across the magneto-resistance, Vout. It is given by

( )0 1out extV R H Iα ρ= +i i i i (5.14)

where R0 is the resistance without external field, ρ is the resistance for unit of length and α is a

fitting parameter accounting for the different orientations between magnetization and current

flowing in the sensor.

The parameters that the designer can change are both geometrical, like the area and the length, but

also structural depending on the magnetic characteristics of the sensor, like the resistance R0 and the

internal magnetic field.

By using the circuit depicted in Figure 5.3-3 it is possible to evaluate the behaviour of the sensor.

Figure 5.3-3: Circuit for reading the output of the magneto-resistance



This circuit consists of a Wheatstone bridge where the resistance R1A and R4A are equal and have

the barber-poles oriented in the opposite direction with respect to R2A and R3A. When the external

field changes, like in Figure 5.3-4 a), the magneto-resistance shows a variation with the magnetic

field, Figure 5.3-4 b). For small value of the external field, the difference output of the Wheatstone

bridge shows a linear variation in the transient response, Figure 5.3-4 c). The output of the sensor is

then amplified by the operational amplifier to make it usable for the following circuit, Figure 5.3-4

d).

Figure 5.3-4: Output of the simulated sensors: a) magnetic field applied, b) resistance of the sensor c) output of

the sensor d) output of the amplifier

5.3.2 Fluxgate

In the Fluxgate sensor a given current through a primary coil induces a voltage on the secondary

(pick-up) coils that depends on the external magnetic field Hext. The output variable is Vout, the

pick-up signal, [28]. The used equations are

1 extH n I Hα= +i i (5.15)

( )2 1tanhsatB B Hβ γ= +i i (5.16)

2out

dBV n l Sdt

= i i i (5.17)

a) c)

b) d)



where H1 is the magnetic field generated by the primary coils, B2 the field on the secondary coils,

and the tanh function is used to model the magnetization curve.

The equivalent circuit used as well as the effect of the magnetic field within the Fluxgate sensor are

shown in Figure 5.3-5.

Figure 5.3-5: Equivalent circuit for the Fluxgate magnetic sensor, and effect of the magnetic field

5.3.3 Hall sensor

In an Hall sensor, the voltage across the Hall terminal depends on the bias current and the external

magnetic field Hext. The output variable is the Hall voltage Vout. It is estimated by

( )01H H extR R R H= + Δi i (5.18)

out H biasV R I= i (5.19)

where RH is the Hall resistance and ΔR0 the resistance sensitivity to the magnetic field.

Figure 5.3-6 shows different equivalent configurations for the Hall devices, in which the source is a

current generator and different terminal of the sensor are used. For every configuration the output of

the amplifier is shown and it is possible to notice that all the configurations are comparable and lead

to the same results, thus validating the sensor model itself.



Figure 5.3-6:Different output for different test configuration for the hall device

5.3.4 Magneto-inductance

In the magneto-inductance the external magnetic field Hext changes the value of the inductance. We

use as output variable the voltage across the inductance, Vout, [29], [30]. It is estimated by using the

set of equations

extH n I Hα= +i i (5.20)

( )tanhsatB B Hβ γ= +i i (5.21)

out

dBV n l Sdt

= i i i (5.22)



where α accounts for the possible loss of magnetic field; β = µ0µr/Bsat is proportional to the

magnetic permeability in vacuum, γ is the residual magnetic field due to hysteresis, n the number of

coils per unit of length, l the length of the sensor and S the area.

To test the sensor the circuit shown in Figure 5.3-7 has been used.

Figure 5.3-7: Magneto-inductance test circuit

The circuit works as follows, assuming that the comparator output is zero. The output voltage rises

at the power-on until the voltage across the resistance R2 exceeds the positive threshold voltage

Vth1; the output of the comparator switches down and the current flows in the opposite direction.

The oscillation depends on the time constant of the R/L series and is (R1+R2)/L.

The simulations results are shown in Figure 5.3-8 and Figure 5.3-9, which report the variation of the

frequency of operation due to the effect of the magnetic field both at the input and at the output of

the comparator, respectively.



Figure 5.3-8: Input of the comparator

Figure 5.3-9: Output of the comparator

5.3.5 Temperature drift

The temperature effect is taken into account with a polynomial equation that enables non-linear

responses. The polynomial parameters are given in the model card. The effectiveness of the model

has been verified using a relaxation oscillator that incorporates a magneto-inductance. Figure 5.3-10

shows the variation with temperature of the frequency of operation for a given constant magnetic

field. The result, that matches experimental data, shows significant temperature dependence. Thus,

an electronic interface will be required to correct the drift using the signal from a temperature

sensor.



Figure 5.3-10: Temperature effect over the frequency

5.3.6 Parasitic effects

The effect of package, solder dot and, more in general, parasitic elements affect the output response

of the sensors. Their effect is taken into account with an equivalent circuit, that includes parasitic

capacitances and parasitic contact resistances. Figure 5.3-11 shows the circuit used to take into

account parasitic effects. Obviously, their importance depends on the circuit where the sensor is

used. For example, for the relaxation oscillator circuit there is a significant dependence on the

parasitic capacitance (Figure 5.3-12) and resistance (Figure 5.3-13).

Figure 5.3-11: Block diagram of the model including solder and package effects



Figure 5.3-12: Effect of the parasitic capacitance on the frequency in the relaxation oscillator

Figure 5.3-13: Effect of the parasitic resistance on the frequency in the relaxation oscillator

Observe that the effect of the resistance worsens the response of the sensor more than the variation

of parasitic capacitance.

5.3.7 Dispersed field

The model enables specifying the amount of magnetic field concatenated by the sensor. A suitable

parameter takes into account the loss in magnetic field. The loss decreases the sensitivity of the

sensor and its value controls the overall gain.



5.3.8 Hysteresis

When a magnetic sensor uses a ferromagnetic material it is necessary to account for hysteresis, [31],

[32]. The effect comes from the magnetization of domains that do not completely de-magnetize

when the magnetic field goes to zero. Moreover, when all the magnetic domains are oriented in the

direction of the external field the value of B saturates. The model of the B-H response use Eq.5.23

as primary function. It assumes that B depends as tanh on H but through the parameters Bsat, δ, β

permits scaling and shifts as well as slope variations. Bres,i, and Hres,i are 0 for the first magnetization

cycle.

The B-H function is recalculated every time the magnetic field reaches a given value and reverses.

The value reached by the magnetic field, Binv,i, defines a new pair of Bres,i and Hres,i. They are

calculated according to Eq.5.24. The use of Eq.5.25 with the pairs Bres,i and Hres,i, and -Bres,i and -

Hres,i, defines two primary functions. They are suitable for the new B(H) response in the positive or

negative quadrants of the B-H plane. The transition between the two functions as H changes its sign

is done with a weighting function W(H) that is Fermi-Dirac like.

( ) ( ), , , ,, , tanhp Res i Res i sat Res i Res iB H B H B H H Bδ β= + +i i i (5.23)

,1, , ,; tanh Res i

Res i Inv i Res iSat

BB B H

Bδ δ − ⎛ ⎞

= = ⎜ ⎟⎝ ⎠

i i (5.24)

( ) ( ) ( ) ( ) ( ), , , ,, , 1 , ,p Res i Res i p Res i Res iB H W H B H B H W H B H B H= + − − −⎡ ⎤⎣ ⎦i i (5.25)

Parameter β takes into account the temperature effect and size of the sensor. The algorithm is light

and easy to simulate. Figure 5.3-14, 5.3-15, and 5.3-16 show simulation results. They represent

several cycles starting from the first magnetization curve. The model also enables local hysteresis

loops.



Figure 5.3-14. Examples of simulations with hysteresis

Figure 5.3-15: Local hysteresis loop



Figure 5.3-16: Complete hysteresis curve from the initial non magnetized condition to saturation


In order to validate the model, a magneto-inductance sensor has been emulated and compared with

a real device available commercially, shown in Figure 5.4-1. The circuit depicted in Figure 5.4-2

has been realized with discrete elements, where R1 = 13.5 kΩ, R3 = 100 kΩ, R4 = 4.7 kΩ, R5-8 =

0.2 kΩ, R9-12 = 100 Ω, 74AC08 element is a AND logic gate, 74HC4066 is a transmission gate

and TLC37021 is a LinCMOS micro-power voltage comparator, Figure 5.4-2.



Figure 5.4-1: The PNI magneto-inductance sensor

Figure 5.4-2: The schematic of the circuit used with the magneto-inductance



Figure 5.4-3: The PCB for the magneto-inductive sensor

To change the external magnetic field Helmholtz coils have been used. By means of a counter it has

been possible to determine the frequency and then the inductance of the sensor for the applied

magnetic field.

In

Figure 5.4-4 the measurement results obtained on the PNI sensor and the simulations results are

reported. Simulations results with and without the hysteresis introduced in the model are depicted.



150

170

190

210

230

250

270

-800 -600 -400 -200 0 200 400 600 800

Magnetic Field [uT]

Freq

uenc

y [k

Hz]

=Experimental data Simulation with hysteresisSimulation without hysteresis

Figure 5.4-4: Comparison between the real sensor and the modelled one

It is possible to notice that in the case of the sensor model without hysteresis an error that is lower

than 10% compared with the real sensor has been obtained, while the error is lower than 5% with

the magnetic hysteresis enabled.

5.5 Conclusions

An Analog High-level Description Language (AHDL) model has been developed. The model has

been written in VERILOGA language for SPICE-like simulators.

The model describes the behaviour of four different types of sensors: a magneto-resistance, a

magneto-inductance, a hall device and a Fluxgate. The symbol of the magnetic sensor is a 5

terminal component, the meaning of some of the terminals can change depending on the type of

sensor. For all the models we have two main terminals, one of which is the reference, representing

the electrical output of the sensor, the other, the differential voltage across the main terminals or the

current through them is the effect of the magnetic field. A voltage generator or a current source

through a third terminal represents the input of the device: the external magnetic field, H. The



remaining two terminals serve for the pick-up coil in Fluxgates or for monitoring internal quantities

for other sensors.

Performance depends on a number of parameters that are specified in the model card similarly to

what is done for active components. The global parameters for a given type of sensor are given in

the sensor model card, in which it is possible to set all the characteristic of the model, from the

physical dimension to the second order effect, like the temperature, the solder effect, the dispersed

field or the hysteresis.

The sensor has been compared with a real sensor to verify the correctness of the used equations.

The sensor show a very good match with the real one and a maximum error in the order of 10%

with the hysteresis disable, and 5% with the hysteresis enable.

The model is suitable to help the designers in the CADENCE environment providing the designers

with a tool that can simulate a real sensor behaviour. Indeed, the developed model has been

intensively used to design the Fluxgate magnetic sensor interface circuit described in Chapter 6.



Chapter 6 Interface Circuit for Fluxgate Magnetic Sensor


Chapter 6

Interface Circuit for Fluxgate Magnetic Sensor

In this chapter an interface circuit for fluxgate magnetic sensors will be presented and discussed. In

particular, the circuit developed for the magnetic sensors, together with the simulation results and

the layout of the chip will be described. Finally, the experimental results and the measurement setup

will be shown.

6.1 The interface circuit design

The interface circuit presented in this chapter is suitable to supply and read-out a double axis micro

fluxgate magnetic sensor devoted to detect the Earth magnetic field.

The basic idea is to realize an interface circuit able to drive different types of fluxgate integrated

magnetic sensors with different characteristics. In particular, the dc resistance and excitation current

has to be taken into account as critical issues. The adopted solution has to allow a proper and smart

testing considering the instrumentation available nowadays and, in addition, it has to be cheap,

[33],.

The main advantages of an integrated solution are essentially related to its low power consumption

and small area occupation, without significant losses in performances. For this reason, the goal of

this project is to develop an integrated CMOS front-end circuit which guarantees the performance

required both in terms of noise and power consumption, in order to meet the requirements explained

above. The solution proposed will be useful not only for a System on Chip (SoC) or microsystem

solution, but also for a System on Package (SoP) or micromodule approach. Indeed, if the magnetic

sensor previously mentioned is realized in a separate die it is possible to bond together on the same

substrate the two dies. In this way the micromodule approach can be implemented, [34], [35].

It is worth to point out that, in the open literature, front-end circuits for fluxgate sensors are

typically based on a sinusoidal or pulsed excitation. By contrast, the approach followed in the

circuit discussed in this chapter uses a triangular current to feed the excitation coil and a

synchronous demodulation for reading-out the voltage induced in the sensing coils. This solution

represents a trade-off between the low-noise performance achieved by the solutions based on



sinusoidal excitation and the simple implementation of solutions based on pulsed excitation, [36],

[37], [38].

Figure 6.1-1: Block diagram of the complete micro-system for magnetic field sensing

The block diagram of the entire micro-system is shown in Figure 6.1-1. The circuit is realized in a

standard 0.35-μm, 2 poly, 4 metal levels, high resistivity polysilicon CMOS process with high

voltage module (25-V devices).

The sensor,[28] used to test the front-end circuit, has been realized in a 0.5-μm CMOS technology

with a post processing step for the ferromagnetic material deposition which includes copper metal

lines for the excitation coil and aluminium metal lines for the sensing coils. The total area of the

planar copper excitation coil (5.5 µm thickness, 71 turns and 12 µm pitch (8 µm metal width and 4

µm of spacing between two metals) is 1760 x 1760 µm2 and its resistance is about of 123.4 Ω. The

total area for the aluminium sensing coils (1 µm thickness, 66 turns, 3 µm pitch 1.4 µm metal width

and 1.6 µm of spacing between two metals) is 650 x 650 µm2 and their resistance is about 1.84 kΩ.

A photograph of the sensor chip is shown in Figure 6.1-2.



Figure 6.1-2: Microphotography the sensor chip

The front-end circuit can be divided into four main blocks: the timing block, the excitation block,

the read-out unit and the ADC converter.

6.1.1 Timing Block

In order to ensure proper timing for the excitation and read-out blocks, the whole circuit is driven

by a clock at 400 kHz. This clock is internally divided, by a cascade of flip-flops.

The outputs of this timing block are two signals: a 100 kHz square wave signal with its

complementary output, that is used to drive the excitation block, and a 200 kHz square wave signal,



used to drive the read-out block and to realize the second harmonic demodulation, needed to

measure the sensor output. By using a 400 kHz master clock a duty cycle of 50% on both the 100

kHz and the 200 kHz output waveforms can be ensured. A duty cycle different from 50%, indeed,

could compromise the demodulation of the signals produced by the sensing coils and, therefore, it

has to be avoided.

6.1.2 Excitation Block

To periodically saturate the ferromagnetic material deposited over the excitation coil of the planar

fluxgate sensor a triangular current waveform has been used, as shown in Figure 5.2-8. The

excitation circuit consists of two different blocks, with two different power supplies values: the first

is a low voltage block, with a power supply equal to 3.3 V, while the second, realized by employing

high-voltage transistors, uses a power supply as high as 25 V. The first block generates a square

wave with frequency equal to 100 kHz. Moreover, in order to guarantee the programmability of the

system and, hence, the possibility to use it with different kinds of sensors and magnetic materials,

the input of the integrator in not fixed. In this way it is possible to modify the output current,

according to the sensor characteristics and the power consumption requirements. This current is

then integrated, with a two stage amplifier with a capacitor in the feedback loop, in order to obtain a

triangular waveform centered around half of the low power supply. Indeed, an higher excitation

current improves the saturation of the ferromagnetic materials, thus allowing achieving better

results in terms of noise and sensitivity, but with higher power consumption.

The second block, depicted in Figure 6.1-3, consists of an high voltage mirrored operational

amplifier with low-impedance output stage, which receives the triangular waveform at the input

and, by means of a resistive feedback loop (not shown in figure), produces a triangular current at its

output. The mirrored amplifier allows achieving the maximum swing at the output terminal. The

class-AB amplifier output stage is designed to provide all the current required by the sensor. A

decoupling stage between the low-voltage and the high-voltage blocks is necessary to level shift the

triangular wave produced by the low-voltage block around half of the high power supply voltage.



Figure 6.1-3: High voltage mirrored amplifier

The behaviour of the excitation block is the following: for half of the clock period the integrator

input is connected to Vin, while in the other half to –Vin, changing the slope of the triangular wave at

the voltage integrator output. This voltage feeds the trans-conductance amplifier. The peak output

current can be regulated by trimming the value of Vin, according to:

1

1

1 t T

out intV V dt

RC+

= − ∫i i (6.1)

The chip has to work with different sensors with different resistance of the excitation coils, varying

from several tens to few hundreds of Ohm. The highest resistance of the integrated coil (estimated

around 280 Ω) and the possibility to test the sensor with an excitation current higher than ±15 mA

(e.g. ±20 mA or higher) could produce a 6-V output swing. For this reason, the output stage of the

tranconductance amplifier has been supplied with 25 V (HV) and, hence, the use of high voltage

transistors has been required. In addition, in this way, it is possible to separate the power

consumption of the two structures, supplying with high voltage only the power stage and, hence,

reducing the power consumption in the integrator and the amplifiers.



6.1.3 Read-Out Block

The pick-up coils of the planar fluxgate magnetic sensor detect the signal induced by the rising and

falling edges of core magnetizing current, as reported in Figure 6.1-4.

Figure 6.1-4: Effect of the external magnetic field over the sensor

As well known, the frequency of the differential voltage produced by the pick-up coils is twice the

frequency of the excitation current. Therefore, it is possible to extract the information on the

external magnetic field by a synchronous demodulation. The two-channel sensor readout circuit is

able to measure the outputs of the sensing coils and to process the resulting signal. Each channel of

the readout circuit consists of four different blocks. The first block is a gain stage that amplifies

each of the two outputs of the sensing coils by a factor of ten. In the second block the difference

between the two outputs is amplified again by a factor of six and demodulated. In order to ensure a

correct demodulation of the sensor signal and to avoid problems due to the asynchronicity between

the clock and the output of the sensor itself, a quadrature demodulation has been implemented.

Using this technique and adding together the contribution of the two orthogonal signals, it is

possible to avoid errors due to timing misalignments between the readout clock and the output of

the sensor. The readout process is done at a frequency equal to 200 kHz The third block is a second

order Sallen-Key low-pass filter that removes all the high frequency components resulting from the

demodulation and returns a DC value that is proportional to the magnetic field. The difference



between this output and the analog ground is then amplified with programmable gain (from 0 to

100) in the last block.

6.1.4 The ADC

The DC output of the readout chain is finally processed by a 13-bit ADC and delivered in digital

form to the output. A single ADC with a multiplexer for both readout channels has been used. With

the switched-capacitor oversampled 13-bit incremental A/D converter available in the library, it is

possible to have a resolution equal to 13 bit in a range of 1 V centred at 1.65 V, giving a least

significant bit (LSB) equal to 122 μV. This range is flexible and can be adjusted according to the

output of the read-out circuitry. The A/D converter operates at a nominal sampling frequency of 1

MHz providing a full resolution conversion in 8 ms with a power consumption of 950 μW. Thus the

nominal system read-out rate is 100 Hz.

6.2 Simulation Results

The proposed system has been simulated in order to evaluate the overall performance. For this

purpose we have first used, for the excitation block and the readout block, the magnetic model

described in the previous chapter, in order to obtain a good approximation with the real resistance

value of the excitation coils of the actual sensor that loads the excitation circuit.

Then a real signal obtained from the oscilloscope while measuring the actual sensor has been used

as input signal for the readout circuit in Cadence environment for the simulation. The actual input

signal, indeed, shows some non-idealities in the output of the sensors that are not easily

reproducible with an ideal source. The actual signal has been saved in a text file and then used in

Cadence with a specific routine, as shown in Figure 6.2-1. In particular it is possible to notice the

presence of a square waveform superimposed on top of the useful signal, due to a capacitive

coupling between the excitation and the sensing coils. Moreover, it is possible to observe that the

differential sensor signal near the positive or negative edges is very small if compared with the

common-mode noise. Thanks to the difference between the two sensor signals performed inside the

interface circuit, it is possible to eliminate the common mode noise and amplify only the signal

proportional to magnetic field.



Figure 6.2-2 shows the triangular current with the maximum amplitude allowed by the class-AB

push-pull high-voltage output stage, equal to 22 mA, in the case of a resistance for the excitation

coil of about 280 Ω.

The interface circuit has been simulated with different input signals, chosen on the basis of the

signal obtained from the oscilloscope, as shown in Figure 6.2-3. This signal has been modelled for

simplicity as a square wave with the signal superimposed not at the edge but at the centre of the

positive or negative value. The different output signals (Figure 6.2-4) obtained have been used to

evaluate the linearity of the whole readout system with different values of the overall gain. The

linearity error obtained is lower than 1% of the full scale (1.05 V to 2.15 V at the input of A/D

converter). The maximum swing of the circuit ranges between 0.4 V and 2.9 V and is tuneable by

setting the gain of the readout chain, according to the sensor sensitivity.

Figure 6.2-1:Sensor signal from the oscilloscope

Figure 6.2-2: Output current of the excitation block



Figure 6.2-3: Different input signals

Figure 6.2-4: Simulated output swing of the chip



6.3 Layout

The layout of the chip has been realized using a modular approach. In order to avoid any problems

in the realization of the chip the matching between the two read-out channels has been taken

particularly into account. The output stage, depicted in Figure 6.1-3, has been realized with the high

voltage module far from all the other blocks, in order to avoid any heating problems due to the large

current flowing into the output stage. In order to minimize the mismatch at the input differential

pair, inter-digitized transistors have been implemented. Special care has been used for the resistors

of the gain stage, where inter-digitized elementary resistors have been employed to improve the

matching. In additions, dummy cells at the border have been introduced to avoid over etching

problems. The digital and the analog parts have been well separated in order to reduce any noise

coupling. The resulting silicon area occupation turns out to be equal to 1.7mm2. The chip layout is

shown in Figure 6.3-1, where it is possible to note at the top and at the bottom some capacitors for

power supply decoupling and in the center the two readout channels with the A/D converter on the

right side. Above the readout circuit there is the excitation block with the high-voltage class-AB

output stage. Figure 6.3-2 shows the microphotograph of the chip.

Figure 6.3-1: Chip layout

Readout ADC

Digital PADs

HighVoltage

PADs

Analog PADs

Excitation

Circuit



Figure 6.3-2: Microphotography of the Read-Out chip


A custom Printed Circuit Board (PCB) has been realized in order to allow the characterization of

the chip. By using the custom PCB, shown in Figure 6.4-1, it is possible to correctly provide biasing

and power supply voltages to the chip. Furthermore, by employing a logic analyzer, it is possible to

evaluate the digital output of the ADC, in the board we reserved space for the sensor wide apart

from the copper layer in order to allow a correct read-out of the signal inside the Helmhtoz coils.

The measurements performed concern the linearity of the acquisition channel and the correct

reconstruction of the rotation angle of the chip referred to the north-south direction. The

measurements obtained have been compared with the previous results of the stand-alone sensor.

Using a HP spectrum analyzer and a function generator and applying directly a triangular excitation

current with a peak of 18 mA at 100 kHz, the stand-alone sensor shows a magnetic sensitivity of

about 0.45 mV/µT, suitable for detecting the Earth’s magnetic field (±50 µT), while the linearity

error is 1.15% of the full scale. The maximum angle error, when combining the measurements of

the X and Y components of the Earth magnetic field, is 3.4°.

The chip linearity has been first evaluated by using the Helmholtz coils.



Figure 6.4-1: Printed circuit board for the chip

Varying the current flowing into the Helmholtz coils it is possible to generate an external field with

a well-know value. If the sensor is oriented in a direction parallel to this field and perpendicularly to

the Earth’s magnetic field, then it is possible to evaluate only the effect of the external field and

obtain a characterization of the device itself. Figure 6.4-2 shows the output of the ADC from the

negative to the positive saturation as a function of the external magnetic field applied with the

Helmholtz coils, while Figure 6.4-3 shows a zoom in the earth magnetic field zone.

Space for

sensor



Figure 6.4-2: Digital output of the sensor as a function of the magnetic field generated from saturation to

saturation

Figure 6.4-3: Digital output of the interface circuit as a function of the applied magnetic induction in the Earth

field range



The system response is linear in the range of ±60 µT, with a maximum non-linearity error of about

3% of the full-scale from the ideal fit, as shown in Figure 6.4-4. The sensitivity obtained is 11

LSB/µT, where 1 LSB is equal to 125 µV.

Figure 6.4-4: Linearity error as a function of the applied magnetic induction

In order to evaluate the performance of the micro-fluxgate sensing system as a compass, the device

has been rotated in an horizontal plane, while exposing it to the Earth magnetic field. Figure 6.4-5

shows the digital output of the two axes of sensitivity as a function of rotation angle. The angle

error is smaller than 4° and includes signal non-linearity, hysteresis and noise, as well as the

inaccuracy of the setup.



Figure 6.4-5: Digital output as a function of the rotation angle

6.5 Conclusion

In this chapter we have shown an interface circuit for double axis micro-integrated fluxgate

magnetic sensors.

The system consists of an excitation block capable to supply a magnetic sensor, featuring a

resistance of 280 Ω in the excitation coil, with a current equal to 22 mA.

The two readout channels allow the interface circuit to correctly process the signal form the sensor.

The possibility to digitally program the gain of the readout chain makes this interface circuit able to

work with different sensors featuring different sensitivities.

The internal demodulator allows the chip to work also in the presence of misalignments between the

sensor output and the readout clock, increasing the robustness of the whole system.



The most important features of the device are summarized in Features of the proposed chipTable

6.1.

Parameter Value

Sensor technology 0.5 µm CMOS

Ferromagnetic core Vitrovac 6025 X deposited by DC-magnetron

sputtering

Interface circuit technology 0.35 µm CMOS

Sensor area 3.2 mm2

Interface circuit area 1.7 mm2

Supply voltage 3.3 V–26 V

Magnetic field range ±60 µT

Linearity error 3% of full scale

Angle error (Earth magnetic field) 4°

Table 6.1: Features of the proposed chip

The application areas for the developed micro-sensor interface circuit are wide. Good sensitivity,

small dimensions and low power consumption from low power supply voltage make it interesting

for portable applications where it is possible to interface the circuit directly to the digital domain.

These applications include:

• Electronic compass;

• Magnetic field detection for medical applications;

• Current measurement;

• Vehicle recognition and other.



127

Conclusions In this thesis we have presented a Wilkinson A/D converter able to resolve 10 bit. The main

characteristic of the converter are:

• INL lower than ±0.5 LSB;

• DNL lower than ±0.6 LSB;

• Clock frequency 50 MHz;

• Input range 1.2 V centered around 1.5 V;

The basic block of the Wilkinson converter are: an input buffer, a comparator, an output register, a

ramp generator, a counter and a clock generator.

For every channel of the pixel matrix there is available one Wilkinson converter, so special care has

been taken in order to limit the power consumption of the whole chip. Since the Wilkinson

converter will not serve only one pixel but sixteen it is possible to slightly increase the current of

the converter without worst the power budget.

The converter has been measured to evaluate the linearity inside the acquisition chain, obtaining a

total DNL and INL lower than ±0.2LSB and ±3.3LSB with a resolution equal to 10bit.

The offset and gain spread between the different channels show no substantial differences, that

suggests that their variation between channels have a negligible effect with request to the variation

within the RPCs..

The power consumption of all the chain is estimated in 0.9mW while the one of the converter stand

alone is about 0.4mW.

A 10 bit pipeline without clock for X-ray spectrometry is presented. The converter is able to convert

an input signal in 2.5μs and achieves 8 bit of resolution. The structure consist of 9 1.5 bit for cell

and a final flash of 2 bit. The structure acts like a combination logic, where the second stage starts

to convert the output of the first stage before that the output itself is stable, and the same also for the

third stage and so on. A SOC/EOC generator is needed to indicate when the conversion is finished.

The total characteristics of the converter are reassumed in the following outline:

• Power consumption 95mW ([email protected] )

• DNL ± 1.2LSB

• INL ± 2.5LSB

• SNR 55.4 dB

• Conversion time 2.50μs

128

An Analog High-level Description Language (AHDL) model has been developed. The model has

been written in VERILOGA language for SPICE-like simulators.

The model describes the behaviour of four different types of sensors: a magneto-resistance, a

magneto-inductance, a hall device and a fluxgate. The symbol of the magnetic sensor is a 5 terminal

component, the meaning of some of the terminals can change depending on the type of sensor. For

all the models we have two main terminals, one of which is the reference, representing the electrical

output of the sensor. Thus, the differential voltage across the main terminals or the current through

them is the effect of the magnetic field. A voltage generator or a current source through a third

terminal represents the input of the device: the external magnetic field, H. The remaining two

terminals serve for the pick-up coil in flux-gates or for monitoring internal quantities for other

sensors.

Performance depends on a number of parameters that are specified in the model card similarly to

what is done for active components. The global parameters for a given type of sensor are given in

the sensor model card, in which it is possible to set all the characteristic of the model, from the

physical dimension to the second order effect, like the temperature, the solder effect, the dispersed

field or the hysteresis.

The sensor has been compared with a real sensor to verify the correctness of the used equations.

The sensor show a maximum error in the order of 10% with the hysteresis disable, and 5% with the

hysteresis enable.

The model is suitable to help the designers in the CADENCE environment providing the designers

with a tool that can simulate a real sensor behavior. In this chapter we have shown an interface

circuit for double axis micro-integrated fluxgate magnetic sensors.

The system consists of an excitation block capable to supply a magnetic sensor, featuring a

resistance of 280 Ω in the excitation coil, with a current equal to 22 mA.

The two readout channels allow the interface circuit to correctly process the signal form the sensor.

The possibility to digitally program the gain of the readout chain makes this interface circuit able to

work with different sensors featuring different sensitivities.

The internal demodulator allows the chip to work also in the presence of misalignments between the

sensor output and the readout clock, increasing the robustness of the whole system.

The most important features of the device are summarized in Errore. L'origine riferimento non è

stata trovata.Errore. L'origine riferimento non è stata trovata..

The application areas for the developed micro-sensor interface circuit are wide. Good sensitivity,

small dimensions and low power consumption from low power supply voltage make it interesting

129

for portable applications where it is possible to interface the circuit directly to the digital domain.

These applications include:

• Electronic compass;

• Magnetic field detection for medical applications;

• Current measurement;

• Vehicle recognition and other.

130

129

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Acknowledgments/Ringraziamenti

Ph.D thesis, Andrea Rossini

134

Acknowledgments/Ringraziamenti Dopo questo lavoro di tesi, desidero ringraziare le persone che hanno reso possibile questa

esperienza, prima di tutto il Prof. Piero Malcovati e il Prof Franco Maloberti, per avermi dato la

possibilità di lavorare nel loro laboratorio ed avermi aiutato ogni volta che ne avevo bisogno, la loro

competenza e professionalità mi è stata di grande aiuto in questi tre anni di dottorato. Un grazie va

rivolto anche al Prof. Andrea Baschirotto, anche in lui ho trovato un valido e prezioso aiuto. Oltre

alla professionalità e alla competenza con queste persone si è instaurato anche un rapporto di stima e

amicizia che spero vivamente continuerà anche fuori dall’ambito universitario.

Ringrazio anche il prof. Enrico Dallago con cui ho collaborato nell’ultimo anno di attività, e il prof.

Guido Torelli con cui invece ho lavorato il primo anno e i loro rispettivi team.

Desidero ringraziare poi tutte le persone del laboratorio di microsistemi integrati con cui ho

condiviso questi anni di studio/lavoro/divertimento, dai dottorandi anziani quando ero io un

dottorando giovane, ai dottorandi giovani quando ero io quello anziano. Ringrazio anche gli amici

degli altri laboratori, quelli incontrati e conosciuti alle conferenze, o semplicemente in giro. Tra tutti

Fausto (il maestro), Ivan (l’aiutante), Paolo (Paolissimo), Cristina (team digitale e analogico

assieme), Luca (il Picoz), Max (Svezia) e Max (dali), Roberto (www.zampironi.org), Marco

(l’uomo magnetico), Edo (arredo), Ale (carbons) e Ale (il vero Ale…).

Desidero ringraziare gli amici della compagnia di Mede, Ottobiano, Pavia e Garlasco, teo (il Teo),

Marco con cui ho condiviso parte dell’esperienza in Olanda e Andrea suo cugino per i suoi preziosi

consigli/conoscenza della geografia europea, Trama e Aleandro(il pilota), Paolo per le tantissime

domeniche al mare, Giacomo (vendimi la tua casa al mare) e Fra (magicosnafuz).

Ovviamente gli altri non si sentano esclusi se non li ho citati direttamente, la lista diventerebbe

troppo lunga. Ringrazio tutti vivamente e di cuore.

Infine dedico il mio lavoro di tesi e ringrazio i miei genitori e le mie sorelle. Il motivo è sempre il

solito: hanno sempre creduto in me anche quando non ci credevo io. Mi hanno sempre aiutato e

spronato quando volevo smettere e mi hanno dato la forza di andare avanti nonostante tutto quello

che è successo. Se non fosse per voi, non starei scrivendo queste righe. GRAZIE!!

Acknowledgments/Ringraziamenti

Ph.D thesis, Andrea Rossini

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Design of mixed analog/digital interface circuits for ... · Design of mixed analog/digital interface circuits for sensors and micro-systems ... in this thesis is the design of integrated