MULTI-FUNCTIONAL INTEGRATED SENSORS FOR THE ENVIRONMENT C.L. Roozeboom1, J.Y. Sim1, D. Wickeraad2, B. Dura1, W.S. Smith2, M.A. Hopcroft2, P.G. Hartwell2,

R.S. Williams2, B.L. Pruitt1 1Stanford University, Stanford, California, USA

2Hewlett-Packard, Palo Alto, California, USA ABSTRACT

We present multi-functional integrated sensors for the environment (MFISEs) combining ten sensor functions on a single silicon die. The purpose of the MFISEs chip is to monitor important environmental parameters such as temperature, humidity and air speed, along with acceleration in three axes. Through a common fabrication process and integrated sensor design, the ten sensing functions required only five photolithography mask steps. To our knowledge, MFISEs demonstrates the highest degree of sensor fusion on a single die. Sensor integration is a key enabler of new applications in mobile electronics and wireless sensor networks, and the potential to use MFISEs for ten sensing functions provides significant cost, size, and power savings over ten individual devices. INTRODUCTION

A decade ago, advances in sensor hardware and communication technology led to the theorizing of a “Smart Dust” paradigm: networks of mobile nodes that would sense the physical world [1]. Key to the vision of Smart Dust is a reduction in the size, power, and cost of these mobile nodes. Size reduction is paramount to reducing manufacturing costs, enabling integration into space constrained platforms such as mobile phones, and making nodes easy to deploy. Minimizing power consumption enables long term wireless use of the nodes without maintenance, and opens the potential for self-powered nodes that harvest energy. Lowering the cost of the nodes enables the feasibility of large scale networks and the application in consumer technology. Since the genesis of the Smart Dust concept, wireless sensor networks (WSNs) have emerged for detailed monitoring in applications ranging from structural health monitoring [2], to habitat and environmental monitoring [3], to parking space occupancy [4]. Technology advances in wireless communications, data storage, and analytics are enabling the use of WSNs in a wider range of applications and will change how we interact with the world. Hewlett-Packard has outlined a vision to create a Central Nervous System for the Earth (CeNSE) that connects sensors to communication networks, databases, and analysis engines to create a planetary-scale awareness [5].

Microelectromechanical systems (MEMS) are widely used in WSNs and advances in manufacturing, materials, and design are reducing sensor size, power, and cost. An additional opportunity for savings is the integration of multiple sensing functions onto a single device. The integrated circuit (IC) industry understands that integration reduces the size, cost, and power of ICs and enables the miniaturization of computers and mobile technology, but the sensor community has yet to widely apply this concept. Discrete sensor components for measuring parameters such as light intensity, humidity, or

temperature are available commercially, but few combined sensors exist to provide multi-parameter information. Academically, Hagleitner et al. demonstrated a single-chip that combined mass-sensitive, capacitive, calorimetric, and temperature transducers for multi-parameter gas chemical analysis [6]. Boisen et al. used piezoresistive AFM probes as reconfigurable sensing platforms [7], and Lee et al. have shown a combined temperature and humidity sensor [8]. However the idea of combining diverse functions in a highly integrated sensor has not yet been explored.

We present multi-functional integrated sensors for the environment that combine ten sensor functions on one silicon die. MFISEs monitors important environmental parameters such as temperature, humidity and air speed, along with acceleration in three axes. We achieved this integration by compromising from the ideal design and fabrication requirements for each sensor to enable ten different sensor functions. The cost, size and power savings of a single integrated sensor and application specific integrated circuit (ASIC) is substantial when compared to ten individual sensors and measurement circuits. The integration of MFISEs enables applications in mobile platforms where space requirements are stringent. This also opens the application space of a wireless sensing node by increasing the number of parameters that can be measured, thus enabling a generically useful sensing device. FABRICATION AND DESIGN

MFISEs devices were fabricated on 4 inch (100) silicon-on-insulator (SOI) wafers (8.5 μm device layer, 1 μm buried oxide layer, 500 μm handle wafer). The fabrication process showing four sensor functions that are representative of the sensor chip is presented in Figure 1. The p-type background doping of the device layer provided a starting sheet resistance of 5 Ω-cm. In one set of wafers, the background doping level was increased to 0.2 Ω-cm by p-type diffusion doping and annealing for 24 hour at 1100o C. A second set of wafers was ion implanted with a dose of 8 x 1014 cm-2 at 180 keV and then annealed for 24 hours at 1100o C for a resistivity of 0.05 Ω-cm. This provided three device layer resistivities as a means to test the optimal background doping level for the MFISEs design.

Alignment marks are patterned in the silicon (Fig. 1i) and 25 nm of screening oxide is grown by thermal oxidation. The thermal oxide is patterned and etched to open up windows for diffusion doping (Fig. 1ii). The piezoresistors and other doped structures are fabricated by n-type diffusion in POCl3 gas at 800o C for 40 minutes for a lower dopant concentration and 850o C for 20 minutes for a higher dopant concentration. The screening oxide and borosilicate glass that forms during diffusion are

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etched in a buffered oxide etch (BOE) solution (34% NH4F, 7% HF, 59% water). The wafers are thermally oxidized again to grow a 1 um thick oxide that is patterned and etched in BOE (Fig. 1iii). The thermal oxide acts as a passivation layer for the piezoresistors and electrical isolation from the substrate for the anemometers, band gap temperature sensor, Hall Effect sensor and metal resistance thermal detectors (RTDs). After high temperature processing, TSUPREM simulation modeling shows the junction depth and peak concentration should be 0.36 μm and 8.6x1018 cm-3 for the low doped wafers and 0.44 μm and 2.0x1019 cm-3 for the high doped wafers. Next, a 1 μm layer of aluminum is sputtered onto the surface, patterned, and plasma etched using a chlorine based chemistry (Fig. 1iv). The device layer is then patterned and etched from the front side using deep reactive ion etching (DRIE) to define the geometry for the accelerometers and humidity sensor (Fig. 1v). Trenches 10 um wide are etched through the device layer to the buried oxide to electrically isolate each sensor. The front sides of the device wafers are bonded to a backing wafer using Crystalbond. The backing wafer serves as a structural and protective layer for backside DRIE to pattern the pressure sensor membrane and etch underneath the anemometers and accelerometers. The buried oxide layer is then plasma etched from the backside to release the anemometers, accelerometers, and pressure sensor cavity (Fig. 1vi).

We use a laser saw to score and then cleave the wafers to separate the sensor die. The pressure and humidity sensors require post-processing steps performed on individual die (Fig. 1vii). The pressure sensor reference cavity is sealed at room temperature and ambient pressure by epoxy bonding the backside of the sensor chip to a PLCC package. The humidity sensor operates on the dielectric change of a polymer layer due to water absorbed from the atmosphere. For proof of concept testing, SPR220 photoresist is manually applied to the humidity sensor comb fingers with a syringe. The sensor die is heated on a hot plate to 70o C for 10 minutes to

Table 1: MFISEs image label and transduction principles Sensor Functions Fig. Transduction Principle Temperature h,i resistance thermal detector

l band gap temperature sensor

Humidity c dielectric change of polymer

Light intensity m photodiode b doped resistance

photodetector Pressure e strain gauge on membrane Air speed- x a hot wire anemometer Air speed- y a hot wire anemometer Accelerometer- x f electrostatic comb fingers Accelerometer- y g electrostatic comb fingers Accelerometer- z d electrostatic comb fingers

k piezoresistive cantilever Magnetic field j Hall effect sensor

evaporate the photoresist solvents. Finally the sensors are placed in a package and wire bonded for testing. DESIGN AND OPERATION The individual sensors operate on a variety of transduction principles. In order to integrate ten sensor functions, we had to balance competing design and fabrication parameters so that each sensor would function and provide useful measurement performance. The MFISEs design demonstrates that compromise from the ideal specifications can enable sensor integration. A summary of the measured sensor performance is given in Table 2.

Figure 2. Process flow of four representative sensors.

SOI wafer, 8.5 μm device layer, 1 μm BOX, 5 Ω-cm sheet resistance

Background doping, screening oxide patterning, diffusion doping

Thermal oxidation and patterning for passivation

Metal sputtering and patterning

Front side DRIE to define mechanical elements and isolation trenches

Back side DRIE and oxide etch to release mechanical elements

Packaging, wire bonding, and polymer drip

Figure 1. Image of MFISEs chip with sensors labeled corresponding to Table 1.

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Temperature is measured with five separate temperature sensors to test the performance of different transduction principles and sensor specifications: a band gap temperature sensor that uses the temperature dependence of the forward voltage drop of two PN diodes, two aluminum RTDs with nominal resistance of 5 kΩ and 25 Ω and two doped silicon RTDs with a nominal resistance of 5 kΩ and 100 Ω (Fig. 2).

Humidity is transduced by a change in the dielectric constant of a polymer layer as it absorbs and desorbs moisture from ambient air. The polymer is applied between electrodes of an inter-digitated comb finger capacitor.

Light is measured in two ways, using a PN junction photodiode and a photoresistor that are exposed on light in the chip. The photodiode is operated in the photovoltaic mode where the light generated current feeds into a trans-impedance amplifier to provide a voltage output. The photoresistor operation is based on the excitation of carriers in doped silicon by incoming light. The resistance of the photoresistor was measured using a Wheatstone bridge.

The pressure sensor consists of four piezoresistive strain gauges at the edges of a square single crystal silicon membrane. The strain gauges are designed so that the two longitudinal piezoresistors increase in resistance and the two transverse piezoresistors decrease in resistance as the membrane deflects downward. The strain gauges are connected in a Wheatstone bridge configuration and the signal is amplified using an instrumentation amplifier.

Air speed is detected by measuring the convective heat transfer of two perpendicular aluminum hot wire anemometers. The differential output of the perpendicular wires gives a measure of the relative direction of the fluid flow.

Acceleration in three axes is sensed by a change in capacitance from the movement of suspended comb finger structures. The x and y-axis accelerometers are identical in geometry and oriented orthogonal to each other. Displacement of the proof mass causes a gap change of the comb finger electrodes. The capacitive z-axis accelerometer uses a proof mass that displaces out of the plane of the sensor chip, causing a change in overlap of the comb finger electrodes. An additional z-axis accelerometer uses piezoresistance strain gauges on a released cantilever proof mass.

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Fig. 3: Calibration data for the band gap temperature sensor (left) and 100 Ω doped Si RTD (right).

Fig. 4: Humidity sensor time series data.

Fig. 5: Light sensor calibration.

Fig. 6: Pressure sensor calibration.

Fig. 8: Piezoresistivie z-axis accelerometer calibration curve (left) and X and Y axis accelerometer output at 1 Hz oscillation.

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Finally, the strength of a magnetic field normal to the sensor surface can be detected by the Hall Effect sensor. The sensor employs a doped Van der Pauw structure commonly used to measure the resistivity and Hall Coefficient of semiconductor samples.

Table 2. Straight line fit sensitivity and power dissipation at 1V bias for each sensor function

Sensor Measured Sensitivity

Power (mW @ 1 V bias)

Band gap 0.5oC/mV 0.1 Al RTD 3.5o C/Δ%R 0.2 Si RTD 6.7o C/Δ%R 0.2 Humidity 1%RH/0.04pF 0.1 Photodiode 1.6 V/W/m2 0 Photoresistor 0.2 V/W/m2 0.3 Pressure 2.1mV/Vbias/Torr 0.4 Air speed- x 25 mV/ Vbias/m/s 5 Air speed- y 25 mV/ Vbias/m/s 5 Accelerometer- x 0.8 V/g 0.1 Accelerometer- y 0.8 V/g 0.1 Accel- z, cap. 1.5 V/g 0.1 Accel- z, PR 70 mV/ Vbias /g 0.8 Hall effect 15 mV/Vbias/mT 2

CONCLUSIONS We have presented the highest degree of sensor function integration yet demonstrated on a single die. The combined sensor function design and fabrication in a unified process flow optimizes the number of functions and sensitivity while reducing the complication and cost

of the device. The reduction of size, power, and cost of sensors through integration is essential for advancing wireless sensor networks and mobile platforms. Future work will focus on performance improvements and integration of energy harvesting structures. ACKNOWLEDGMENTS This work was supported by the Hewlett-Packard Labs Innovation Research Program Grant, a Hewlett-Packards research and education gift grant, and the NSF Center of Integrated Nanosystems Grant No. ECCS-083281. Work was performed in part at the Stanford Nanofabrication Facility (a member of the National Nanotechnology Infrastructure Network), which is supported by NSF Grant No. ECS-9731293, its lab members, and the industrial members of the Stanford Center for Integrated Systems. The authors would like to thank J.C. Doll and N. Harjee for discussions and assistance with fabrication and process development. REFERENCES [1] Kahn, J. M., R. H. Katz, et al. (1999). “Next century

challenges: mobile networking for 'Smart Dust'”, ACM/IEEE International Conference on Mobile Computing and Networking, Seattle, WA USA.

[2] Ning, X., R. Sumit, et al. “A wireless sensor network for structural monitoring”, ACM International Conference on Embedded Networked Sensor Systems, Baltimore, MD USA, 2004.

[3] Alan, M., C. David, et al. “Wireless sensor networks for habitat monitoring”, ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, GA USA, 2002.

[4] Tubaishat, M. and S. Madria. "Sensor networks: an overview." Potentials, IEEE, 22(2): 20-23, 2003.

[5] Sutter, J. D. "'Smart dust' aims to monitor everything." CNN Tech, 2010.

[6] Hagleitner, C., A. Hierlemann, et al. "Smart single-chip gas sensor microsystem." Nature, 414(6861): 293-296, 2001.

[7] Boisen, A., J. Thaysen, et al. "Environmental sensors based on micromachined cantilevers with integrated read-out." Ultramicroscopy, 82(1-4): 11-16, 2000.

[8] Chia-Yen Lee and Gwo-Bin, L. "Micromachine-based humidity sensors with integrated temperature sensors for signal drift compensation", Journal of Micromechanics and Microengineering, 13(5): 620, 2003.

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