1048 IEEE SENSORS JOURNAL, VOL. 10, NO. 6, JUNE 2010 Passive

1048 IEEE SENSORS JOURNAL, VOL. 10, NO. 6, JUNE 2010

Passive Wireless Monitoring of Wafer CleanlinessDuring Rinsing of Semiconductor WafersXu Zhang, Jun Yan, Bert Vermeire, Member, IEEE, Farhang Shadman, and Junseok Chae

Abstract—Semiconductor facilities consume large amounts ofwater, most of which is used for rinsing of wafers during cleaningsteps. To optimize water use, real-time and in situ monitoring ofwafer cleanliness during rinsing is necessary. Yet no prior art isreal-time and in situ. In this paper, we present a passive wirelesssensing system capable of measuring the residual contaminationon the wafers in real-time and in situ. The wireless system consistsof a micromachined Electro-Chemical Residue Sensor (ECRS)and wireless transponder circuitry based on RFID technology.The ECRS measures the impurities concentration of the waterinside micro-features during the rinsing processes. The sensorreading is converted to a frequency by the on-wafer transponderand remotely logged through a wireless link between two coupledinductors, while the transponder captures power from the remoteRF signal: a battery-free system. A prototype system was fabri-cated and its frequency output was characterized by using sodiumchloride solution ranging from 190 ppb to 6 ppm. The measuredwireless chemical sensor system has a relative uncertainty of lessthan 1% in the concentration range.

Index Terms—Passive sensing, rinsing monitoring, wafer rinse,water saving, wireless sensing.

I. INTRODUCTION

A SEMICONDUCTOR facility consumes a large amount ofwater, 4 to 10 million liters per day [1]. More than 80%

of the consumed water is used for wafer rinsing after chemicalcleaning, necessary to remove cleaning chemicals prior to thesubsequent processing steps [2]. Control of the rinse process iscurrently performed by ex situ measurement of remaining con-taminants [3] or by monitoring the resistivity of the effluent [4].The former method measures the remaining contaminants onthe wafer and can only be performed after the rinse and dry pro-cesses are complete. Such ex situ measurements do not prop-erly elucidate the dynamics of the rinse process. The latter ap-proach monitors the effluent and does not provide contaminantconcentration on the wafer surface, where it actually matters.

Manuscript received October 26, 2009; revised January 15, 2010; acceptedJanuary 15, 2010. Current version published April 02, 2010. This work was sup-ported in part by the National Science Foundation (NSF) I/UCRC ConnectionOne Research Center at Arizona State University and Semiconductor ResearchCorporation (SRC). The associate editor coordinating the review of this paperand approving it for publication was Prof. Kiseon Kim.

X. Zhang, B. Vermeire, and J. Chae are with the School of ElectricalComputer and Energy Engineering, Arizona State University, Tempe, AZ85287-5706 USA (e-mail: [email protected]; [email protected];[email protected]).

J. Yan and F. Shadman are with the Department of Chemical and Environ-mental Engineering, University of Arizona, Tucson, AZ 85721-0011 USA(e-mail: [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSEN.2010.2042443

Hence, current rinsing processes are generally based on experi-ence and guesswork. As a result, expensive high-purity deion-ized water is usually overused to ensure complete rinsing. More-over, it is difficult to monitor the cleanliness inside high as-pect ratio micro-features, which are often present in semicon-ductor and microelectromechanical systems (MEMS) devices.These high aspect ratio micro-features are the bottleneck of therinsing process. This motivates our research work: real-time andin situ monitoring technology for optimizing and controlling therinsing process for patterned wafers.

Previously, we have developed a micromachined Electro-chemical Residue Sensor (ECRS) to monitor cleanliness ofwafers [5], [6]. A cross-section schematic of the ECRS is shownin Fig. 1(a). The sensor is a micromachined resistivity sensor. Itfeatures a dielectric layer covering the electrodes which distin-guishes it from a traditional micromachined resistivity sensorwith bare electrodes [7], [8]. With the thick covering dielectriclayer, the ECRS measures the impedance of fluid inside highaspect ratio micro-features which mimics the cleanliness ofpatterned wafers. The ECRS requires complementary circuitsto read and analyze. Since the electrical wires, connectors andbatteries do not survive the cleaning chemistries used duringsemiconductor manufacturing, it is preferable to have themonitoring system to be wireless, passive (remotely powered)and integrated.

In this work, we present a prototype system that extendsa RFID technology to read out the ECRS data, as shown inFig. 1(b). It allows real-time, in situ, passive and wirelessmonitoring of wafer cleanliness. The output of the ECRSis converted to a frequency via an on-wafer oscillator andtransmitted to outside reader by passive transponder circuitryvia inductive coupling. This new tool may offer new rinserecipes with low resource usage and significant cost savings forsemiconductor facilities [5].

This paper is organized as follows. In Section II, the overviewrequirements and system architecture are described. Decom-posed designs of each element are presented and discussed inSection III. In Section IV, we show the measurement results ofa prototype wireless system and related system performancesare discussed. Finally, the conclusion remarks are presented inSection V.

II. SYSTEM OVERVIEW

The specifications of the rinsing process monitoring are listedin Table I. The ECRS operates at low concentration (10 ppb~10 ppm), corresponding to high impedance (in the order of

) between the two electrodes [6]. Fig. 2 showsthe characterized impedance profile for corresponding impurity

1530-437X/$26.00 © 2010 IEEE

Authorized licensed use limited to: Arizona State University. Downloaded on June 01,2010 at 19:27:03 UTC from IEEE Xplore. Restrictions apply.

ZHANG et al.: PASSIVE WIRELESS MONITORING OF WAFER CLEANLINESS DURING RINSING OF SEMICONDUCTOR WAFERS 1049

TABLE IDESIGN REQUIREMENTS AND ENVIRONMENTS OF THE SYSTEM [1], [9]

Fig. 1. (a) Schematic of ECRS. (b) Sensing data and power are transferred viainductive coupling. (c) Schematic of wireless monitoring system.

concentrations using the ECRS. The “wafer clean” is evaluatedwhen the impurity concentrations is less than ,(semiconductor foundries have their own specifications, thenumber used here serves only as an example) corresponding

Fig. 2. Rinsing profile of the ECRS and rinsing quality evaluation.

to the impedance of higher than 200 . Based on the rinsingprofile, the rinsing recipe could be optimized and a customizedthreshold impedance could be assigned to determine the rinsingend point accurately.

The impedance of the ECRS is modulated and wirelesslytransmitted by inductive coupling (near-field coupling) as it issimple and widely applied in RFID applications [9], [10]. In oursystem, we chose the widely allowed and the lowest frequencyband, around 13.56 MHz, among the Industrial Scientific Med-ical (ISM) bands, since inductive coupling is more effective atlower frequency [11], [12].

Similar to widely adopted RFID schemes [13]–[15], thecarrier is modulated by resistive modulation. Other modulationmethods exist including capacitive modulation, in which acapacitor is added in series with the switch to modulate carrierphase. However, the capacitive modulation involves a com-plex architecture and has lower target signal power [16]. Theschematic of the resistive modulation in our wireless systemis illustrated in Fig. 1(c). In situ monitoring is performedfrom the measurement of the ECRS, while real-time wirelessdata transfer is enabled by the RF transponder circuitry. TheECRS measures the impurity concentration of the rinsing waterthrough its electrical impedance ( ). The oscillator convertsmeasured impedance ( ) to the oscillation frequency ( ).The oscillator controls the load modulation switch, in parallelto the secondary inductor ( ). As a result, the load ofchanges with the frequency, . Due to the load modulation,a modulated signal is generated and transmitted wirelessly tothe primary inductor ( ). The signal received at the readeris demodulated by either spectrum analysis or a demodulatorto obtain the oscillation frequency, . Using a calibratedcurve, the impurity concentration is extracted from . Allnecessary power is harvested from the external carrier. Therectifier generates a DC voltage to supply the oscillator.



Fig. 3. (a) Parasitics of ECRS. (b) Sensor impedance with frequency. (c) Inter-digitated electrodes to minimize parasitics.

III. SYSTEM DESIGN

A. Electrochemical Residue Sensor (ECRS)

A brief design of ECRS is described in this section, while de-tails are discussed in our prior publications [5], [6]. The ECRSmeasures the resistivity inside high aspect ratio micro-features.The equivalent circuit of the ECRS is illustrated in Fig. 3(a),including parasitic elements [6]–[8]. is the resistance ofmicro-features between two electrodes. This is the target pa-rameter of the sensor and reflects the water resistivity, which isrelated to the impurity concentration. is the charge transferresistance and is double layer capacitance, both of whichare introduced to account for the solid-liquid interface chem-istry. and are the capacitors formed due to the dielectriclayers above and below the electrodes, respectively. , ,and are resistances of the substrate, electrodes and impuri-ties above the wafer, respectively. Including the parasitics, thesensor impedance is expressed as

(1)

Impedance model of the sensor could be developed basedon (1). Details in modeling are discussed in [5] and [6]. Thefrequency characteristics of the ECRS impedances for differentconcentrations could be obtained by applying sensor parametersin (1) and plotted in Fig. 3(b). dominates the impedance atlow frequency, while the impedance is dominated by and

at high frequency. Only at intermediate frequency is theimpedance dominated by the solution concentration sensitiveterm, . In the low-frequency range, which is dominatedby , the impedance also varies with concentration. TheGouy-Chapman model states that stems from two layers:the Stern layer and the diffusion layer [17]. The capacitanceof Stern layer ( ) is independent of concentration, whilecapacitance from diffusion layer ( ) varies with solutionconcentration since the effective thickness of the layer, theDebye length, is a function of the concentration. However, thesensitivity of to the impurity concentration is smaller thanthat of , as illustrated in Fig. 3(b), therefore, we choseas the target sensing parameter.

The ECRS is designed to maximize the width of the inter-mediate frequency band where dominates the impedance.This is achieved by increasing and decreasing and .Decreasing and is limited because it is not feasible to de-posit a very thick dielectric film. Control of is more feasibleas it is proportional to the area of liquid-electrode interface. Toincrease the interface area and thereby increase , the sensorelectrodes are elongated in an interdigitated pattern as shown inthe scanning electron micrograph (SEM) in Fig. 3(c). The opti-mized sensor has a working frequency range of a few hundredshertz to a few tens of kilohertz.

B. Oscillator

The oscillator converts the ECRS impedance to the oscillationfrequency. As shown in Fig. 4(a), the oscillator output charges/discharges capacitor through the ECRS. , and arebiasing resistors and generate a reference voltage ( ) at thenon-inverting input of the amplifier. The oscillations of voltagesare illustrated in Fig. 4(b). When the output is high, , thereference voltage is at . Then, is charged

(2)

where is the RC time constant resulting from and theimpedance of the ECRS. When is charged to nearly ,the amplifier output becomes low, 0 V. Then, is dischargedthrough the ECRS. During the discharging phase, the referencevoltage drops to and voltage of becomes

(3)

When the voltage of approaches , the amplifieroutput becomes high and thus repeats charging and discharging.From (2) and (3), the time , for to be charged toand the time , for to be discharged to can be calcu-lated as

(4)



Fig. 4. (a) Oscillator converts sensor impedance to oscillation frequency.(b) Voltage changes of the oscillator.

Since is a function of sensor impedance ( ), the oscillationfrequency is a function of the ECRS impedance

(5)

C. Inductive Coupling

The frequency-encoded ECRS impedance data is transferredto the reader via inductive coupling between the inductors atboth the reader and transponder ( and ). The operatingprinciple of the data transfer is described using the systemimpedance model shown in Fig. 5. If the impedance change of

resulting from the coupling to is modeled as equivalentimpedance ( ), the total impedance at the reader side canbe expressed as [11]

(6)

where is the working frequency, is the resistor to measurethe target signal, is the resistance of and is the series

Fig. 5. Model of the system with inductive coupling.

capacitor that tunes the reader’s resonance to 13.56 MHz. Whenthe switch is off, becomes

(7)

where is the coupling coefficient between the two inductors,is the resistance of , is the input resistance of the

rectifier, and are the side lengths of the two inductors andis the distance between the two inductors. When the switch is

on, becomes

(8)

As a result, the voltage across , , varies with theswitching frequency . The amplitude variation of is afunction of the power of the target signal and is proportional tothe difference between and .

Obviously, to obtain stronger target signal, it is desired to havelarge impedance difference between and , whichis a function of the quality factor of the transponder inductor.

can be expressed in terms of the quality factor of , :

(9)

Comparing (8) with (9) shows that in order to increase the dif-ference between and , needs to be high, whichcould be achieved by decreasing and increasing .

D. Rectifier

The rectifier converts RF input power to a DC voltage, whichpowers the oscillator. Most RFID designs have a voltage dou-bler as a rectifier [18], [19]. The input impedance of a traditionaldoubler, the impedance of a forward biased diode, is small, a fewhundreds of Ohms. Such small impedance greatly decreases thetransponder’s quality factor ( ) and consequently lowers thetarget signal power, as shown in (7)–(9). Therefore, we put alarge resistor ( in Fig. 6) in series with the diode network.cannot be too large as a large resistor decreases the voltage dropacross the diode network ( ) and consequently decreasesthe DC voltage at the output of rectifier. Voltage doublers can be



Fig. 6. Two-stage voltage doubler as rectifier with high input impedance.

Fig. 7. Fabricated passive and wireless transponder.

cascaded to obtain a higher DC output. However, the number ofthe stages directly affects the rectifier efficiency and DC outputvoltage. Generally higher DC output can be achieved by in-creasing the number of stages, yet compromising rectifier effi-ciency [16]. In practice, two to four stages are typically used. Inour design, we chose a two-stage rectifier to achieve relativelyhigh efficiency.

IV. CHARACTERIZATION AND DISCUSSION

The fabrication of the wireless sensor system uses a standardmicromachining technology. On an oxidized silicon substrate,polysilicon and silicon dioxide layers were deposited byLPCVD (Low Pressure Chemical Vapor Deposition). Bothlayers were patterned by RIE (Reactive Ion Etching) using thesame mask to form desired micro-features and electrodes ofthe sensor. The secondary inductor was electroplated to reduceits resistance for high . The primary inductor, oscillator,rectifier and necessary discrete components were implementedon a PCB. Soldered wires were used to connect the secondaryinductor to the transponder circuitry and to the ECRS. Theassembled on-wafer transponder including the secondary in-ductor, ECRS and transponder circuitry is shown in Fig. 7.

The characterization of the system was performed in twostages: characterization of the ECRS and characterizationof the overall wireless system with a controlled calibration

solution. The ECRS was measured at 10 kHz where the resis-tance of solution inside the micro-feature ( ) dominates theimpedance. The characterization was performed with sodiumchloride solution with concentration from 190 ppb to 6 ppm.Concentrations less than 100 ppb were not used as the conduc-tivity of low ionic concentration solutions is difficult to controlbecause from the air dissolves in the water and raises theconductivity. Such measurements should be performed in awell-isolated environment, which remains as our future work.Sodium chloride was used to demonstrate the functionality ofthe system. Different semiconductor foundries have differentrinsing recipes. In order to actually implement the wirelesssystem in semiconductor foundries, it is necessary to calibratethe system with specific rinsing solution cocktails. An exampleof the calibrated curve of the ECRS is shown in Fig. 8(a). Theimpedance changes from 85.4 to 44.5 as the concentra-tion of sodium chloride increases from 190 ppb to 6 ppm. Thecalibration curve presents the measurements uncertainties ofless than 0.3%.

System level characterization was performed with thesodium chloride solution of the same concentration range. Thesystem has a working distance of approximately 5 cm. In theexperiment, the distance between the two inductors was setat 1 cm. The RF input was provided by a signal generatorwith 1 V at 13.56 MHz. The oscillation frequency wasmeasured using a signal source analyzer. A spectrum of thedetected signal for 600 ppb NaCl is shown in Fig. 8(b). Theoscillation frequency is the frequency difference between thecarrier, 13.56 MHz and the first sideband peak. High-orderharmonics are generated as the modulation switch is controlledby square wave rather than pure cosine wave. In Fig. 8(c), themeasured oscillation frequencies are plotted as the solutionconcentration varies from 190 ppb to 6 ppm. The measurementuncertainties are within 1%.

The system resolution is a function of RBW (ResolutionBandwidth) of the signal source analyzer. In our measure-ments, the signal source analyzer, Agilent E5052A, was used,which has a minimum RBW of 1.53 Hz. With the minimumRBW, the detected signal is analyzed from 13.564425 MHzto 13.564525 MHz (100 Hz band), as shown in the embeddedfigure in Fig. 8(b). The frequency resolution is approximately



Fig. 8. (a) Characterization of the ECRS as a function of sodium chloride con-centration (measurement uncertainties are within 0.3%). (b) Spectrum of de-tected signal. (c) Characterization of system as a function of sodium chlorideconcentration. (The measurement uncertainties are within 1%.)

0.4 Hz, which corresponds to a concentration resolution of20 ppt at a concentration of 190 ppb. However, the price paid tohave high resolution is the measurement time. To comprise themeasurement time and measurement resolution, we used theRBW of 97.7 Hz in our measurement, which has a frequencyresolution of approximately 2 kHz (concentration resolution of0.4 ppb at a concentration of 190 ppb).

The system was measured a few times with an interval of 12hours to measurement its reproducibility. Ambient temperaturewas well controlled within . Measurement resultsare shown in Fig. 9, which shows a system reproducibilityof 1.5%. No obvious system drift is observed from thesemeasurements.

Before practical use, temperature effect on the system shouldbe considered. Temperature is known to drift conductivitysensor by 1%–3% per degree [7]. To study the temperatureeffect, the wireless system was mounted on a hotplate and

Fig. 9. Repetitive measurements show the reproducibility of the system is 1.5%for different concentrations. (Error bars show the measurement uncertainties arewithin 1.4%.)

Fig. 10. Temperature coefficient of the system is measured as 1%/ C fordifferent concentrations. (Error bars show the measurement uncertainties arewithin 1.6%.)

TABLE IISYSTEM SPECIFICATIONS

thermocouples at different places around the system were usedto measure and control the temperature within C. Tem-perature was varied from 26 C to 41 C with a step of 5 C.System responses for different concentrations at different tem-peratures were recorded in Fig. 10. The temperature coefficientof the system was measured approximately 1%/ C. Generally,semiconductor manufacturing is performed in a well controlledenvironment, temperature fluctuations will not greatly affectsthe monitoring system. However, if the system is designed towork with large temperature variations, corresponding calibra-tions at desired temperatures are needed to have a more preciseresponse from the system.

In a complete system, the transponder wafer could be usedas a monitoring wafer that undergoes the same procedures as



regular wafers do during the rinsing process. A protective film,e.g., PDMS, epoxy, should cover the circuitry to be protectedfrom chemical attacks. Such a fully functional system can mon-itor the rinsing progress in real-time and in situ to develop op-timized rinsing recipes and consequently large amount of ultra-pure water could be saved, up to 40%–50% [5].

V. CONCLUSION

The semiconductor industry consumes a large amount of ul-trapure water for rinsing wafers. Current methods are unableto provide real-time and in situ sensing data during rinsing foroptimizing and reducing water usage. We present a passive andwireless chemical sensing system prototype to achieve real-timeand in situ measurements of water cleanliness via RFID and mi-cromachining technologies. All necessary power is supplied bythe RF signal and high accuracy sensing data is transferred wire-lessly using inductive coupling. The potential outcome includessaving large amounts of water in semiconductor and MEMSfacilities, resulting in environment friendly and cost effectiveproduction.

REFERENCES

[1] J. DeGenova and F. Shadman, “Recovery, reuse, and recycle, of waterin semiconductor wafer fabrication facilities,” Environm. Progress, vol.16, pp. 263–67, 1997.

[2] S. N. Kempka, J. R. Torczynski, A. S. Geller, J. J. Rosato, R. N. Walters,and S. S. Sibbett, “Evaluating the efficiency of overflow wet rinsing,”Micro., vol. 41, pp. 41–46, 1995.

[3] K. K. Christenson, “Rinsing: A critical process in contamination re-moval,” J. Inst. Environ. Sci., pp. 45–50, 1997.

[4] I. I. Kashkoush and R. E. Novak, “Water consumption and rinse con-siderations in semiconductor wet processing,” Proc. Electrochem. Soc.,vol. 95–20, pp. 421–425, 1995.

[5] J. Yan, K. Dhane, B. Vermeire, and F. Shadman, “In situ and real-timemetrology during rinsing of micro- and nano-structures,” Microelec-tron. Eng., vol. 86, pp. 199–205, 2009.

[6] J. Yan, D. Seif, S. Raghavan, H. J. Barnaby, B. Vermeire, T. Peterson,and F. Shadman, “Sensor for monitoring the rinsing of patternedwafers,” IEEE T. Semiconductor M., vol. 17, pp. 531–7, 2004.

[7] D. He, M. A. Shannon, and N. R. Miller, “Micromachined silicon elec-trolytic conductivity probes with integrated temperature sensor,” IEEESensors J., vol. 5, pp. 1185–1196, 2005.

[8] P. Jacobs, A. Varlan, and W. Sansen, “Design optimization of planarelectrolytic conductivity sensors,” Med. Biol. Eng. Comput., vol. 33,no. 6, pp. 802–810, 1995.

[9] B. Jiang, J. R. Smith, M. Philipose, S. Roy, K. Sundara-Rajan,and A. V. Mamishev, “Energy scavenging for inductively coupledpassive RFID systems,” IEEE Trans. Instrum. Measure., vol. 56, pp.118–125, 2007.

[10] C. Klapf, A. Missoni, W. Pribyl, G. Hofer, G. Holweg, and W.Kargl, “Improvements in operational distance in passive HF RFIDtransponder systems,” in Proc. IEEE RFID 2008, 2008, pp. 350–357.

[11] K. Finkenzeller, RFID Handbook, 2nd ed. London, U.K.: Wiley,2003.

[12] J.-P. Curty, N. Joehl, C. Dehollain, and M. J. Declercq, “Remotely pow-ered addressable UHF RFID integrated system,” IEEE J. Solid StateCircuits, vol. 40, pp. 2193–202, 2005.

[13] Q. Huang and M. Oberle, “A 0.5-mW passive telemetry IC for biomed-ical applications,” IEEE J. Solid-State Circuits, vol. 33, pp. 937–946,1998.

[14] S. Preradovic, N. C. Karmakar, and I. Balbin, “RFID transponders,”IEEE Microw. Mag., vol. 9, no. 5, pp. 90–103, 2008.

[15] M. K. Khaw, F. Mohd-Yasin, and M. I. Reaz, “Recent advances in theintegrated circuit design of RFID,” in Proc. IEEE Int. Conf. Semicon-ductor Electron., 2004, pp. 326–330.

[16] G. De Vita and G. Iannaccone, “Design criteria for the RF section ofUHF and microwave passive RFID transponders,” IEEE Trans. Mi-crow. Theory Tech., vol. 53, pp. 2978–2990, 2005.

[17] B. Timmer, W. Sparreboom, W. Olthuis, P. Bergveld, and A. van denBerg, “Optimization of an electrolyte conductivity detector for mea-suring low ion concentrations,” Lab Chip, vol. 2, pp. 121–124, 2002.

[18] R. E. Barnett and J. Liu, “An EEPROM programming controller forpassive UHF RFID transponders with gated clock regulation loopand current surge control,” IEEE J. Solid-State Circuits, vol. 43, pp.1808–1815, 2008.

[19] T. Umeda, H. Yoshida, S. Sekine, Y. Fujita, T. Suzuki, and S. Otaka, “A950-MHz rectifier circuit for sensor network tags with 10-m distance,”IEEE J. Solid State Circuits, vol. 41, pp. 35–41, 2006.

Xu Zhang received the B.S. degree in Electronic En-gineering from Tsinghua University, China, in 2006.He is currently a PhD student in the School of Elec-trical Computer and Energy Engineering at ArizonaState University. His research interests include wire-less sensing, MEMS integration with electronics andmicro-packaging.

Jun Yan received the B.S. degree in chemical engi-neering from the Wuhan Institute of Chemical Tech-nology, Hubei, China, in 1996, the M.S. degree inChemical Engineering from South China Universityof Technology, Guangzhou, China, in 2001, and thePh.D. degree in chemical engineering from the Uni-versity of Arizona, Tucson, in 2006.

From 1996 to 1998, he was an Assistant Engineerat Wuhuan Chemical Engineering Corporation. Since2007, he worked at the University of Arizona as aPostdoctoral Researcher. In 2008, he joined Environ-

mental Metrology Corporation as a Research Scientist.

Bert Vermeire (M’02) received the M.S. degree in electrical engineering fromthe Catholic University Leuven, Leuven, Belgium, in 1991 and the Ph.D. degreein electrical engineering from the University of Arizona, Tucson, in 1999.

He was a member of the Ultra Clean Processing Group at IMEC, Belgium,between 1991 and 1995, a Research Assistant at the University of Arizona be-tween 1995 and 1999, and a Postdoctoral Research Scientist at the University ofArizona from 2000 until 2002. He joined Ridgetop Group in 2002 as Chief Tech-nical Officer. He is currently an Associate Professor of Research and Directorof the Wireless Nanotechnology programs at Arizona State University. He isalso a Co-Founder and Chief Technical Officer of the Environmental MetrologyCorporation.

Dr. Vermeire is a member of the Electrochemical Society and has served asan Adjunct Faculty Member at the University of Arizona.

Farhang Shadman received the Ph.D. degree from the University of California,Berkeley, in 1972.

He is a Regents Professor of Chemical and Environmental Engineering (jointappointment in Optical Sciences) at the University of Arizona. He is also the Di-rector of the Engineering Research Center for Environmentally Benign Semi-conductor Manufacturing and a Co-Founder of the Environmental MetrologyCorporation. Prior to joining the University of Arizona in 1979, he was a Re-search Engineer at the General Motors Research Laboratory in Michigan. He isthe author of more than 90 journal articles, 3 book chapters, and 15 patents/in-vention disclosures.

Prof. Shadman received the Akira-Inoue Award (2002), the Landmark In-novation Award (2000), the SRC/Sematech/SIA Excellence in Research Award(1998 and 1992), and the Invention Awards (SRC 1991 and 1992). He is a Fellowof American Institute of Chemical Engineers.



Junseok Chae received the B.S. degree in metallur-gical engineering from the Korea University, Seoul,Korea, in 1998, and the M.S. and Ph.D. degrees inelectrical engineering and computer science fromthe University of Michigan, Ann Arbor, in 2000 and2003, respectively.

After a couple of years of being a Research Fellowat Michigan, he is now at Arizona State University asan Assistant Professor of Electrical Engineering. Hehas published over 55 journal and conference articles,two book chapters, and holds two U.S. patents. His

areas of interests are MEMS sensors/actuators, MEMS integration with elec-tronics, micro-packaging, and bio-MEMS.

Prof. Chae received the First Place Prize and the Best Paper Award at theDesign Automation Conference in the student design contest in 2001. He is arecipient of NSF CAREER award.


Documents

1048 IEEE SENSORS JOURNAL, VOL. 10, NO. 6, JUNE 2010 Passive