ADI JAN1608

Slide 1:

Gary Cocker:

Good morning, good afternoon, or good evening, depending on where

you are in the world, and welcome to today’s webinar, “Noise

Optimization in Sensor Signal Conditioning Circuits.” This is

part one, presented by Analog Devices. I’m Gary Cocker. I’ll be

your moderator today. We have just a few announcements before we

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our technical support helpline, which is also located in the

webcast help guide. Now, onto the presentation, “Noise

Optimization in Sensor Signal Conditioning Circuits," part one.

Noise is a term that is broadly used in signal path design, but

in many cases there is a level of confusion as to what it exactly

means and how to deal with it. Today, in this first of a two-

part series, we will explore the topic in detail and get insight

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and answers on the sometimes intimidating subject of noise.

Making this presentation today is Reza Moghimi, applications

engineering manager in Analog Devices Precision Amplifier Group.

Reza, welcome, and over to you.

Reza Moghimi:

Thanks, Gary. Let’s start. There have been many papers written

on the topic of noise and, as you said, still there is -- the

term is used broadly with some levels of confusion. A person

yelling is asked not to make too much noise. A person standing

next to a highway might say that it is too noisy. Some engineers

think of DC errors, such as offset voltage and bias current as

noise, but others refer AC parameters such as current noise and

voltage noise densities as noise. We will establish a definition

of noise from an electrical engineering point of view. But for

now, generally speaking, we can say that noise is any unwanted,

undesirable signal that affects the quality of the useful

information.

Slide 2:

So I know that my audience today comes from a very diverse

background. Some of you come from a digital background and are

not familiar with the analog concepts, and some of you are very

strong in analog design. But I can assure you that you will be

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getting something out of this and the next session, which is

scheduled for the month of February. If I do not get to all

questions or give satisfactory answers to your questions, or

don’t know the answer to your questions, I promise you that ADI

has enough experts in this field that I can consult with and get

back to you, so please do not hesitate to send us email. So

after explaining why design for low noise and defining noise and

its types, I will introduce a general formula to calculate noise

of a signal condition circuit. Next, I will introduce a low

noise design process, and in my February session I will explain

deeper into this low noise design process and share with you dos

and don’ts, and a number of ways to use and optimize common

signal conditioning circuits.

Slide 3:

So let’s see what changes have come about and why, more than

ever, every system designer needs to know about low noise design.

So, why low noise?

Slide 4:

Let’s look at the types of applications that are in the

mainstream these days and need low noise signal conditioning. A

typical signal chain is shown over here. On the left, there are

many sensor types, and the rest of the circuit is a single

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conditioning path. Making high-resolution measurements

accurately depends on the noise floor of the system. A parameter

I’m sure you’re all familiar with, called an S signal to noise

ratio, gives a good idea as to how much noise we have in a

system. So the question is, what are the major sources of noise?

Is it the sensor, signal conditioning circuits themselves, or a

pickup or radiated noise, as it is called? Remember that sensors

have their own noise and they are supposed to detect small

signals which cannot be distorted. So to attain best noise

floor, designers need to understand component levels noise

sources, pick the best signal processing architecture, and

prevent external noise sources that might interfere with the

application circuit.

Slide 5:

Why low noise signal conditioning? Popular applications have

moved toward lower operating supply voltages. It used to be

common to have +/- 22V, and now it’s pretty common to have +/-

0.9 V or +/- 1.5 V, and more applications are requiring higher

precision and accuracy. As an example, car industry has moved

from an 8-bit system to 12 bits or higher. Lower supply

operations, combined with higher accuracy requirements -- meaning

the number of bits -- has made measurements of microvolts quite

challenging. We used to point out the LSB size for 5 V and 10 V

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full-scale data acquisition systems, as it is shown here, and we

used to say that these were very small and we needed low noise

signal conditioning. For example, we used to point out, for a

14-bit system, where the full scale was 5 V, the LSB size was 305

μV. But remember that this is the amount of error that we are

allowed after signal conditioning circuits. The situation is

worse when we look at the output of the sensors, as I showed you

in the previous slide -- signal chain. Sensors have, or produce,

very small signal.

Slide 6:

So, as an example, imagine a real-world signal that generates

signals of 30 mV maximum, full-scale. The half an LSB for a 12-

bit system is 3.5 μV, and if you have 1μV of input referred error

or noise from the amplifier, would invalidate the measurement.

Eliminating noise is very critical since this usually sets the

lower limit of usable signal level in a circuit.

Slides 7, 8, 9:

Something else that we need to know when we have a driver for an

ADC is the signal to noise ratio of an ADC can degrade as the

result of the goodness of the amplifier driving it. Designing

low-noise signal conditioning is critical, since the noise

generated by an ADC driver needs to be kept as low as possible in

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order to avoid worsening the signal to noise ratio. In this

slide, I show how the SNR gets degraded if you pick a wrong

amplifier. As an example, when using the AD7671, which is the

16-bit, 20μV RMS noise and 10 mHz of bandwidth, if you pick this

ADC let’s see how we can degrade its SNR by picking different

amplifiers. Shows us amplifiers that have different noise specs,

and we see the SNR loss as a result of the amplified noise. So

let’s get a better understanding of what noise is, how we define

noise, and what are the noise sources.

Slide 10:

I explained that sensor signals are small, the need for

resolution has gone up, power supply voltages have shrunk, and in

order to make meaningful signal conditioning we need to learn

about low noise design. It is critical to optimize the signal

conditioning circuitry so we get rid of the noise and measure the

real signal. So let’s see how we define noise and what are the

noise types that that we need to worry about.

Slide 11:

So what is noise? One can define noise to fall into two

categories, either an extrinsic or interference, and intrinsic or

inherent. Electrical, magnetic are forms of extrinsic noise.

They can be periodic, intermittent, or random, and system

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designers can reduce the effect of these through a number of

ways. Thermal agitations of electrons and random

generations/recombinations of electron-hole pairs are examples of

inherent noise, which IC manufacturers have tried to reduce by

better processes or better circuit design techniques.

Slide 12:

And intrinsic noise is what I will be talking about today. We

will cover the extrinsic noise and how to deal with it in the

future.

Slide 13:

So I mentioned earlier, in loose terms, that noise is any

unwanted signal. So what is really noise in the electrical

engineering term? Let’s define it. Engineers define noise as a

random process due to quantum fluctuations inherent in all

resistors and semiconductor devices, specifically P-N junctions

that create voltages and currents in any application. Noise is

an instantaneous, has an instantaneous value, and is

unpredictable, it’s possible to predict it in terms of

probabilities, which we will talk about. Most noise sources are

treated as uncorrelated and have a Gaussian distribution. When

it comes to defining noise, we use terms such as peak to peak,

RMS, and we show noise in the peak to peak and, many times, in

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spectral noise density graphs.

Slide 14:

So here is how noise looks like over a 0.1 to 10 Hz, and over a

much wider frequency of 10 Hz to 10 kHz, as it is shown on the

right-hand figure. It is difficult to mathematically

characterize amplifier noise at low frequencies due to 1/f,

temperature, and aging drifts, and possibly even popcorn. This

is why it is easier for us to just show 0.1 to 10 Hz photographs

in our datasheets, and move on. Peak to peak is really only

meaningful for 0.1 to 10 Hz of bandwidth.

Slide 15:

So here is a noise signal, and unlike AC signals, whose power is

concentrated at just one frequency, noise power is spread all

over the frequency spectrum. Instantaneous value of noise is

unpredictable, but possible. It is possible to predict in terms

of probabilities. I’ve shown here -- as I mentioned earlier,

most noise has a Gaussian distribution.

Slides 16, 17, 18:

Derivative of noise power, which is the mean square voltage of

the voltage noise, the frequency is called the noise power

density, and is denoted as En2 or E2 in the case of the voltage

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noise. When specifying noise, as we can see, we should always

specify the frequency band that we are talking about.

Slide 19:

So let’s see what RMS means. Here is how we define RMS

mathematically.

Slide 20:

Let’s say that we have a pure sine wave. To get the RMS, we have

to square the signal, and once we do we get a signal like this.

Slide 21:

We have to do the average. Again, it’s the root mean square. We

have done the squaring. We have to do the mean.

Slide 22:

And this is how it looks like after averaging. If you take the

root of the signal, that’s what happens. So a sine wave in a

root, RMS fashion, is just going to look like as it is shown on

the bottom figure on this slide.

Slide 23:

So the graph on the left shows a peak-to-peak noise value of a

part over the broadband frequencies. It is very difficult to

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read peak-to-peak values accurately and consistently, as I

mentioned, from the graph on the left. When noise power density

is plotted versus frequency, it provides a visual indication of

how power is distributed over a frequency. In ICs, as you can

see on the right-hand figure, the two most common forms of power

density distributions are what is called the 1/f and white noise.

The flat part of the graph is called the white noise, and when it

starts going up it is a 1/f noise. The quantities of the voltage

noise and the current noise are noise spectral densities and

expressed in nV per root Hz and peak A per root Hz. In some

cases, μA per root Hz per root Hz. The noise spectral density

shows the noise energy at a given frequency, while an RMS gives

an RMS value over a given bandwidth or time interval.

Slide 24:

It is always good to know the peak-to-peak noise value. Because

noise is random, there is always a probability the voltage could

exceed the peak-to-peak value. This probability is shown here,

in terms of a new term, crest factor, which we define as the

ratio of the peak-to-peak value over the RMS value of the noise.

RMS values are easy to measure repeatedly and are the most usual

form for presenting noise data. One can use the table above to

estimate the probabilities of exceeding peak values, given the

RMS values. A very common number to convert from an RMS to a

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peak to peak is using a factor of 3.3 or 6.6 times. The

probability of a peak-to-peak value exceeding 6.6 times the RMS

noise value is 0.1%.

Slide 25:

It was mentioned earlier that noise spectral density of an IC is

composed of white noise and 1/f noise. I referred to these two

terms without really going through them. But there are other

contributors to IC noise, and these are popcorn noise, shot

noise, and avalanche noise. We cover all of these parameters one

by one. Also, in addition to ICs, there are other components

such as resistors, capacitors, and inductors, that are commonly

used in system designs and these elements have their own noise

sources for noises. So let’s get a better understanding of each

one of these terms.

Slide 26:

So the white noise mentioned is the flat part of the noise’s

spectral density, as it is shown over here.

Slide 27:

It is also called the broadband noise, and the voltage noise

density is constant over a frequency.

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Slide 28:

And this is how we show it mathematically in the RMS form.

Slide 29:

Remember I mentioned that you need to have the bandwidth

specified, and the f2 minus f1 is the bandwidth that we have in

mind in this example.

Slide 30:

If f1 is a lot smaller than, let’s say, ten times of the f2 --

let’s say we pick f1 to be 100 Hz and the f2 to be 10 mHz or

something, then the f1 is really irrelevant.

Slide 31:

Then we can approximate the noise to be just the En times the

square root of the f2 frequency. Remember, this is the noise

floor of the systems and a limiting factor for system resolution.

This is what is called the broadband noise, or the white noise.

Slide 32:

Another noise term that we define, or we mention, is the pink

noise -- also called the flicker, or 1/f noise. At low

frequencies, as is shown over here, noise goes up inversely

proportional to the frequency. And that’s why the term -- 1/f

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term. One over f is always associated with current and is caused

due to traps, which, when current flows, capture and release

charge carriers randomly, therefore causing random fluctuations

in current itself. In the BJTs, it is caused by contamination

and imperfect surface conditions at the base-emitter junction of

a transistor. In CMOS, it is mostly associated with extra-

electron energy states at the boundary between silicon and

silicon dioxide. The 1/f corner frequency, which is a figure of

merit, is a frequency above which the amplitude of noise is

relatively flat and independent of frequency.

Slide 33:

To measure, or to calculate the 1/f noise, the equations are

shown over here, as you can see. Please note that the corner

frequency for a voltage noise is different, or might be

different, than the corner frequency of a current noise. I

mentioned earlier that the unit for voltage noise is nV/root Hz,

and for a current is sometimes μA per root Hz.

Slide 34:

One characteristic of a 1/f noise is that the power content in

each decade is equal, and this is what is shown in this slide.

As you see, we have bandwidths that are apart from each other by

one decade. I showed you the formula earlier, and squaring both

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sides of that equation gives 1/f noise power, which is

proportional to the log ratio of the bandwidth, regardless of the

band location. So if you pick 200 Hz and 20 Hz is a factor of 10

and the log of 10 is always 1, as I showed in that equation.

Slide 35:

So here is -- some interesting facts, which I like to share with

you. White noise has equal energy per frequency.

Slide 36:

And we mentioned that the f2, when it is very large compared to

f1, RMS noise is set by the f2.

Slide 37:

Pink noise has equal energy per octave.

Slide 38:

RMS noise is set, I showed you in the equation, by the ratio of

the f2 to f1.

Slide 39:

So I just want to give you a little bit of bonus.

Slide 40:

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And that is, I ask you which do you think sounds louder?

Slide 41:

Why do they call it white noise?

Slide 42:

Why do they call it pink noise? I give you the answers for two

of them, and you can give me an answer for another one, for the

third one, later, because I don’t know the answer to that

question myself. White noise sounds louder. It has more energy

at wider bandwidth than pink noise. Our ears are much more

sensitive to frequencies from 500 to 2 kHz. White light has all

frequencies with equal energy from each frequency. White light

has equal energy per frequency. And I mentioned that I don’t

know why it is called pink noise. If you know, please let me

know.

Slide 43:

To give you a feel as to how broadband noise and 1/f noise look

like -- we actually get a lot of questions related to noise all

the time, and one of the things that I usually ask my customers

to do is look at the noise on the scope and interpret the

results, or if they don’t understand they can send us a picture.

So here are those two references that you can use. The upper

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figure is the broadband noise, and the one shown below it is the

1/f noise. The broadband noise is the furry shape, and 1/f noise

is more rough and grassy. That’s how you can say it.

Slide 44:

So now the question is, what happens when we have spectral noise

density and not the peak-to-peak graph in a data sheet? This has

happened sometimes in some of the data sheets that we have done

over the past 40 or 50 years. Here, I show you a formula that

you can use to arrive at the peak-to-peak value.

Slide 45:

Assuming you are given the graph on the left, you can use this

formula. You can find out the corner frequency and, given the

two frequencies for the f1 and f2 of 0.1 and 10 Hz, you can find

out what the peak-to-peak value would be based on the noise

spectral density that you have. If you go through the

calculation, as I have here, you get the peak-to-peak noise to be

218 nV.

Slide 46:

And here is the graph, which is in the data sheet, showing the

peak-to-peak noise to be 200 nV. They come pretty close, and we

have a good idea. We can move on.

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Slide 47:

Another noise term is called, or noise type, is called the

popcorn noise, also called the burst noise. It causes the step

function voltage changes at the output of an amplifier. It is

caused by transistors jumping erratically between two values of

beta. In the early days, popcorn noise was a serious issue that

resulted in random discrete offset shifts in a timescale of a few

tens of milliseconds. Today, although popcorn noise can still

occasionally occur during manufacturing, the phenomenon is

sufficiently well understood and parts are scrapped during

limited testing. Popcorn noise is a part of the 1/f noise and

remember it happens at very low frequencies. It’s purely a

function of the process. It was more of a problem in the old

days and not as big of a deal these days, although every once in

a while you have some issues. We have done extensive in this

area and we have experts who have done great amount of work who

can help in case you need any more information.

Slide 48:

The noise that is called the shot noise, also called a

[Schottky?] noise, occurs whenever a current passes through PN

junctions and there are many junctions, as you know, in our

parts. Barrier crossing is purely random and the DC current

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that’s observed is the sum of many random elementary current

pulses. So this is the current noise, and it has a uniform power

density. It’s a part of white noise. And remember, white noise

is constant over all frequencies. The equation to find the value

of a shot noise is given over here, which is the square root of 2

qI delta f -- I in the case of an amplifier is the Ib that you

are talking about. There is a handy number that you can use if I

is given in the peak amps, which, for our J FET parts and CMOS

parts, the Ibs are in the [pento arm?], you can use the

simplified equation to quickly find out what is the amount of

shot noise based on that current.

Slide 49:

I mentioned that this is when the current passes through a PN

junction, so here I have used -- I give you an example of two

diodes operating at two different currents -- at one micron and 1

nA, and I have calculated the noise based on those currents

crossing the PN junction. As a result, the noise is generated

and the signal to noise -- the signal being the current that I

forced through the diodes and the noise generated as a result of

those currents -- the signal to noise ratio, as you can see, is

65 dB and 35 dB.

Slide 50:

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Another noise type is the avalanche noise, and it is found in PN

junctions in reverse breakdown modes. We don’t really have this

in our parts and I have the slide here to be complete. I just

have a couple of bullets on these. I’m not going to spend too

much time on this.

Slide 51:

The thermal noise, also called Johnson noise, found in all

resistors, even a resistor sitting in a drawer in the lab

somewhere. This noise is generated as a result of temperature

changes or whatever else that might be happening. It’s the

thermal agitation of electrons in resistors that cause random

movement of charge, causing a voltage to appear, and thermal

noise is part of the white noise, or the broadband noise, that we

just talked about earlier. The equation to find the thermal

noise is given right in the middle of this slide, which is the

square root of 4 kTR times the bandwidth that we are working on,

and I have every term defined there. the way to reduce the

thermal noise is, of course, to pick a small resistor -- as it is

shown over here, the smaller the resistor the less noise. If we

can control the temperature, if we can cool the temperature that

will be smaller, and as one of my friends, Mr. James Bryant,

says, there isn’t anything you can do with the Boltman constant

because he’s dead and that constant is fixed. So, remember,

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doubling the resistance increases the noise by 3 dB because it’s

under the square root term. So four times the resistance equals

to doubling the noise.

Slide 52:

One of my characteristics is that I always want to make sure that

we all understand what we are talking about. So here is a

question to you: What is the noise contribution of a 10 k

resistor room temperature?

Slide 53:

Is it A, B, C, or D?

Slide 54:

I give you a hint: Remember that the 1 k resistor has 4 nV per

root Hz of noise.

Slide 55:

So, knowing that information and using this equation, by looking

at this you will find out that the answer is C, which is 12 nV.

And this way you can quickly look at your circuits and your

components that you have picked and find out what the noise of a

resistor is right off the top of your head. So remember, the 1 k

resistor has 4 nV per root Hz and then from that point on it’s

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just the square root of -- you use that if you want to pick the

10 -- square root of 10 is almost 3, 3 times 4 is 12.

Slide 56:

And if you cannot remember the equation or you don’t want to be

bothered, I am giving you a graph here that plots the resistor

noise based on the resistor value and, as you see, as the

resistor value goes up its noise up. It’s very critical to

notice things, because in the next session, when we go and design

the signal conditioning circuit, we need to account for all the

errors, or all the noise sources that come into picture.

Slide 57:

So now the question is, what’s the sum of two noise sources? How

do we add up the two noise sources? If the noise sources are

uncorrelated -- as I mentioned, the majority of the noise sources

are recombined in a root-sum-square as it is shown over here.

Slide 58:

This means that adding tow noise sources that have the same

energy only increases the overall noise by 1.4 or 3 dB.

Slide 59:

That’s the square root of 2, remember that. Two equal noise

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sources added up have a 1.4 factor.

Slide 60:

And this works for audio, as well. Two people talking the same

volume about two totally different things only add 3 cB of sound

pressure levels to the party, and it’s not going to be 2 times as

much. So remember the RSS or the root-sum-square fashion for

adding uncorrelated noises.

Slide 61:

So, let me just say, go back to a signal conditioning circuit

that’s interfacing a sensor and give you a handy formula that you

can always use to calculate the two different noise of your

signal conditioning circuit.

Slide 62:

So what we have done so far is that -- I have explained that all

ICs have some inherent noise within them. In case of an

amplifier, those noise sources can be modeled as zero impedance

voltage generator en noise source in series with the input, and

infinite impedance current sources parallel with the input, as I

have shown in.

Slide 63:

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Do not worry too much about the direction of the arrows here -- I

just drew something. Each of these terms vary with frequency, as

I have shown, and the type of amplifiers that you have picked for

your signal conditioning. I mentioned that the voltage noise

density unit is nV per root Hz and the current noise density is

pA or μA per root Hz. Both of these sources can be treated as

uncorrelated noise sources and the amplifier here that I have

shown, I am treating it as a noiseless amplifier. This is an

ideal amplifier that I wish we had, which we don’t have yet. So

now if you put the ideal amplifier and its noise sources there,

we come up with the signal conditioning as it is shown here with

a sensor, which I show a sensor on the left-hand side of the

figure with its noise resistance and resistor, as we mentioned,

has a noise. All the resistors around the amplifiers have noise

associated with them, as I have shown here. So now to look at

the noise -- the total noise on the input, as it is called, the

refer to inputs, we can use the equation that I show on the

bottom of the slide. In general form, these are all noise

sources that I could think of in a typical signal conditioning

circuit. Total output noise referred to input is given by

resistor noises, op amp noise, which are current and voltage

noise sources. It’s not going to be -- there are some terms here

that need a bit of explanation. And we will talk about these

things later on. What I was just going to point out here is the

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term called noise gain as it is defined, what is called the

bandwidth, or the effective bandwidth, as I explain to you later

on what that is. So if you want to look at the noise on the

output, you have to know the referred to input noise and multiply

that by the noise gain and not really the signal gain. I’ll

explain to you what this is and how we define this.

Slide 64:

So what is really the noise gain? How do we define this? This

is how much the noise and whatever garbage and extra useless

information is gained up by. Regardless of the configuration,

whether the amplifier is an inverting or non-inverting

configuration, the noise gain, as I showed you earlier, is equal

to 1 plus R2 over R1. Here, I show two circuits. The one on the

left-hand side is a non-inverting configuration. The one on the

right-hand side is an inverting configuration. In an inverting

configuration, as I state here, the signal gain is minus R3 over

R4; the noise gain is 1 plus R3 over R4. In the left-hand side,

where we have a non-inverting configuration, as you all know, the

signal gain is 1 plus R2/R1 and the noise is 1 plus R2 over R1.

So in both configurations the noise gain is 1 plus the feedback

resistor over the R1, or actually R4 in this case. Sorry --

there is an error in my drawing here. Instead of R4 over R3, I

had mentioned R2 and R1. Those are easy to understand.

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Slide 65:

And I mentioned the term called noise effective bandwidth. White

noise is passed as if the filters were a brick wall type, but

with a cutoff frequency of 1.57 times as large. The 0.57

accounts for the transmitted noise above the cutoff frequency as

a consequence of gradual roll-off.

Slide 66:

We can think of the amplifier as a single-pole low-pass filter,

which is a good approximation, and that -- you can use the

equations to come up with the noise of a first-order low-pass

filter, meaning you have to multiply the noise by the square root

of 1.57 times that corner frequency.

Slide 67:

And if you want to design a higher order of filters, and just

figure out the noise effective bandwidth, here is the equation

that you can use.

Slide 68:

The N is the order of the filter and once you use this -- I give

you an example here, that if N is equal to 1, as it is the first

order filter that I just explained in the previous slide, the

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factor is 1.57 and as you go higher and higher in the order you

get closer to the brick wall and the bandwidth is narrowed, and

less noise is passed through, which is a desirable feature, but,

again, there is a price to pay to go to a higher-order filter.

You get rid of the noise, but there is a price to pay. That’s

how life is in the amplifier world, and in our world. There’s

always a tradeoff that you have to make.

Slide 69:

I say here that the high-order filter is good and as you can see

it can make an improvement to our signal to noise ratio.

Slide 70:

So assume that I have used the circuit in the previous slide for

the signal conditioning that I showed you, where I showed you the

noise calculation equation and all the noise sources. So assume

that I have used that circuit and I am using a 10 mHz amplifier

in an inverting gain of 1000, using the resistor values of 100 k

in the feedback and 100 ohm as a source, and what I have done

here is I have plotted amplifier noise -- meaning, if I picked

different amplifiers that have different voltage noise densities,

put it in this configuration, the amount of noise that I will get

at the output is just going to look like this. So if you pick an

amplifier that has a large value for its voltage noise density,

26

then the output noise will be higher even if it 10 mHz. So you

need to make sure you’re picking the right component. This is

something that we talk about later on, in the next session. You

have to know how to go about picking the right component, whether

it is a capacitor, a resistor, an amplifier, or anything that’s

just going to be used for signal conditioning. You have to

exercise a good discipline and good practice. And it is very

important to pick the right component, and I explain about this

later on in the next session.

Slide 71:

Here, just to be complete -- here we look at the noise gain of a

second-order system. The shape of the noise gain is going to be

different, of course, because of the resistors because of the

capacitors. At very low frequency, noise gain is a function of

the resistors, as I have shown here, and at higher frequency is

the function of the capacitors. Capacitors do not generate noise

themselves, but the current noise of an amplifier drops across

the capacitor and creates a voltage noise error.

Slide 72:

So how do we go about designing a low noise circuit? My

suggestion is always to build in low-noise design ideas and

concepts, rather than designing something and then trying to

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reduce the noise by shielding, layout, and other techniques that

you try to figure out at the end of your design process. So you

have to build it in. So what process do I suggest?

Slide 73:

So here is the process that I suggest. I will say you have to

know which frequency range or which frequency band are you

interested in? Are you working in the 1/f region? Is your

application requiring a broadband region? That’s the very first

thing you need to understand -- where your signals, or what’s the

bandwidth of your signals. And then you have to design -- my

suggestion is, for the best performance that you can find.

Today’s amplifiers have noise range of 0.9 nV to 60 nV.

Understanding about the input architecture and the amplifier

helps you pick the right amplifiers. So you pick the right

amplifier, you pick the right target components, and you know the

bandwidths. You design around that. Then you worry about the

other things that are non-noise requirements, like the input

impedance or how much current you have to use in your system and

what gain, and stuff like that. And if noise specs is not met

after going through this process, then you need to go back again

and pick a different component, maybe a different amplifier, and

go through this situation several times. So, it’s very important

to understand a little bit about the amplifier -- how it is put

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together, and my goal is to show you in the next few slides how

noise sources inside of an op amp power and what benefits do you

get if you go with a bipolar or a CMOS or a J FET amplifier.

Slide 74:

So what are the noise sources in the bipolar amplifiers? If a

data sheet tells you that the noise is 3 nV, it’s not good

enough. You need to know how these 3 nV is achieved, since, as I

mentioned earlier, there is always a tradeoff that has to be made

and that tradeoff might affect your application.

Slide 75:

So here is the input structure of an amplifier on bipolar

process. Thermal noise, shot noise, and 1/f noise are the three

noise sources that you need to worry about, or a designer needs

to worry about, inside of the power, but you need to be aware of

these things. And voltage noise, I mentioned earlier, is made up

of 1/f and broadband noise.

Slide 76:

So if you want to know the complete story in what factors in

bipolar transistors, or what elements in bipolar transistors,

contribute to the voltage noise density or the voltage noise of a

bipolar op amp, here is the complete equation. And I mentioned

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there is always a tradeoff. And here is the current noise

density equation that you can use as a reference. So, to get,

say, a low-voltage noise density you need to use a very high

beta, as it is shown in that equation, in the denominator. But

that requires a light doping and very thin Rb on the input, and

Gm and Ibs, as you know, are directly proportional to the IC, and

therefore the current noises can go.

Slide 77:

So what works to minimize the en is the opposite of what is good

for low-current noise density, which represents the fundamental

tradeoff in bipolar design.

Slide 78:

There are a number of parts that have super beta or Ib

cancellation circuits in them that introduce correlated noise.

Ib cancelled ports -- not, still, as good as FET for bias

currents, but they bring in a lot of good benefits. This is the

compensation for bias current error term and we have a number of

parts, and I will talk briefly about the compensated parts, what

to do with them, in the next session.

Slide 79:

The noise in CMOS parts -- we have many, many CMOS, many, many J

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FETs, and many bipolar parts that we have designed over the past

40-50 years.

Slide 80:

So when we look at the noise sources inside of a CMOS part, here

is how it looks like. There are three noise associated, and

these are the gate leakage, which I show as the Ing 1/f noise,

which I show as If, and the current noise, which is another shot

noise term, which is shown as Ind.

Slide 81:

Noise contributors are different in different regions of this

graph. There are process dependencies or design tweaks that can

be used to get better noise specs, but each have their

implications on the approximation application level.

Slide 82:

Flicker noise is inversely proportional as an example to the

transistor WL, so to reduce the noise one has to use input stage

transistors with large geometries. So here we see, as we use

larger geometries for W and L in the denominator the noise is

just going to go down, but this has implications that you need to

be aware of when we go to the application level.

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Slide 83:

Bigger transistor geometries are just going to have larger

capacitances that come into the end applications, and you need to

worry about this and compensate the amplifiers correctly to get

the performance that you need to get out of it.

Slide 84:

Here's an equation to measure the current spectral density -- is

just showing you the equation for the corner frequencies. And

what designers can do inside of the IC companies like mine.

Slides 85-86:

We can, of course, operate the FET where gm is large. We can do

that to get lower noise.

Slide 87:

We can use high values of the static current.

Slides 88-89:

Again, we get the noise down but we are consuming a lot of power,

a lot of current, as it is shown over here, or, as I mentioned,

we can use a large geometries to get the noise down. Each one of

these we will talk about later on, as to what implication it may

have in the final design.

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Slide 90:

Briefly, I talk about the corner frequency in CMOS.

Slide 91:

We know where a corner frequency is, where the flat part and 1/f

noise are equal to each other --

Slide 92:

-- and if you recall the equations for the CMOS part you get the

corner frequency if you use this equation. This, again, has

significance to us, as significance to the equation, as I showed

you there, that finding the right corner frequency, or pushing

the lower corner frequency to very close to DC is very important.

Slide 93:

And here is the noise in the J FET. Compared to bipolar

transistors, J FETs have much lower gm. Therefore, FET op amps

tend to have a higher voltage noise for similar operating

conditions. We have some J FET parts that are very low noise,

also, but when we do a one-to-one comparison we can make a

statement like that. And remember, at room temperature, the

current noise density, like the CMOS parts, is not a problem. It

is negligible. Many times in the μA per root Hz -- but one

33

drawback here is that it doubles for every 10 to 20 degrees and

that may become a bit of a problem over temperature, if you need

to operate over wide temperatures, like middle temperature

ranges.

Slide 94:

So, in a tabulated form, here I compare the bipolar, CMOS, and J

FET input amplifiers for the processes that the amplifiers are

on, and I give you an idea as to which amplifier process to pick

if you have an interest in the voltage noise or a current noise,

and this way make it easy for you.

Slide 95:

So, in summary, what I have done is I have explained as to why it

is important to understand the low-noise design. I built some

foundations and fundamentals on the noise definitions and the

noise types that are available. I gave you a general formula

that can be used for an inverting, non-inverting, different sound

amplifiers under many configurations, and how to calculate the

noise of a typical signal conditioning circuit, and I very

briefly introduced you to a low-noise design process. I will go

through these things a bit further in my next talk, and I show

you a lot of dos and don'ts and different ways of optimizing a

typical circuit in my February talk. This ends my talk. Gary?

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Gary Cocker:

Thank you, Reza, for that very nice presentation. We are running

a bit long, so we're going to have to truncate our Q & A a bit.

I'm going to ask you to please bear with us on that score. Reza,

how does popcorn noise behave with time?

Reza Moghimi:

Wow, that's a great question and, actually, we get that quite

often. Occurrence of the popcorn is quite random. An amplifier

may exhibit several pops per second during one observation

period, and then it remains popless for several minutes. We

don't really have a screening -- I don't think anyone has a

screening -- a perfect screening, to remove all noisy poles,

although we have a pretty good understanding that what causes it

and how we can catch the parts with very high probability.

Gary Cocker:

Reza, I'm afraid our production team has given me the time-out

signal. That's all we have time for today. I wish we could get

more questions answered. But I do want to wrap up with --

everyone, we will be getting to all of your questions here. All

of your submitted questions will be answered by Reza via email

shortly after the conclusion of this broadcast. So if you send

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in a question, it is not in vain. It will be answered. So look

to your email for that. And that's going to wrap up our show

today. And thank you for attending today's webinar, "Noise

Optimization in Sensor Signal Conditioning Circuits." This was

part one, presented by Analog Devices. Don't forget to catch

part two, which is going to be scheduled for next month. So I

hope you'll join us there. and, again, look to your email for

your Q & A answers. And thank you very much, Reza, again. For

additional information and documentation, please direct your

attention to the Analog Devices resource page that's opened

before you at www.analog.com. This webinar is copyright 2008 by

CMP Media. The presentation materials are owned and copyrighted

by Analog Devices, Incorporated, which is solely responsible for

its content. The individual speakers are solely responsible for

their content and their opinions. On behalf of our panelist

today, Reza Moghimi, and our entire webinar production team, I'm

Gary Cocker. Thank you for joining us, and have a great day.

END OF TRANSCRIPT

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