INVITED PAPER Microelectrodes, Microelectronics, and Implantable Neural Microsystems Progress in development of tiny electrodes, cables, circuitry, signal processors and wireless interfaces promises to advance understanding of the human nervous system and its disorders. By Kensall D. Wise, Life Fellow IEEE , Amir M. Sodagar, Member IEEE , Ying Yao, Member IEEE , Mayurachat Ning Gulari , Gayatri E. Perlin, and Khalil Najafi, Fellow IEEE ABSTRACT | Lithographically defined microelectrode arrays now permit high-density recording and stimulation in the brain and are facilitating new insights into the organization and function of the central nervous system. They will soon allow more detailed mapping of neural structures than has ever before been possible, and capabilities for highly localized drug-delivery are being added for treating disorders such as severe epilepsy. For chronic neuroscience and neuroprosthesis applications, the arrays are being used in implantable microsystems that provide embedded signal processing and wireless data transmission to the outside world. A 64-channel microsystem amplifies the neural signals by 60 dB with a user-programmable bandwidth and an input- referred noise level of 8 V rms before processing the signals digitally. The channels can be scanned at a rate of 62.5 kS/s, and signals above a user-specified biphasic threshold are transmitted wirelessly to the external world at 2 Mbps. Individual channels can also be digitized and viewed externally at high resolution to examine spike waveforms. The microsystem dissipates 14.14 mW from 1.8 V and measures 1.4 1.55 cm 2 . KEYWORDS | BioMEMS; implant; microelectrodes; microstimu- lation; microsystems; neural probes; neural recording I. INTRODUCTION Of all the parts that make up the human body, the nervous system is by far the least understood and its disorders are the most difficult to treat. Research on the use of electrical stimulation to restore movement to paralyzed limbs goes back at least 250 years to the work of Franklin [1] and others, but it was midway through the last century that physiologists began to use electrical recording to try to understand the nervous system at the cellular level [2]. Electrolytically sharpened metal wire electrodes [3] were used for extracellular recording, while glass micropipettes [4] were used to probe individual cells. Significant progress was made in understanding the behavior of single neurons and some sensory areas of the brain, but it became increasingly clear that understanding the organization and signal-processing techniques used at the system level would require simultaneous recording and stimulation from many sites having known spatial locations in tissueVsomething that existing technology did not then permit. From the 1950s onward, there was also growing interest in developing electronic interfaces to the brain for use in prosthetic devices for treating various disorders. Work on visual prostheses began with Brindley’s early efforts [5], but by the late 1960s, a program to develop a cortical prosthesis for the blind was also under way under Dobelle at the University of Utah [6], [7]. Initial work on cochlear prostheses for the deaf began about the same time [8]–[10]. Most early work involved experiments using wire-based stimulating electrode arrays, but it was quickly recognized that better technology was needed, not only for Manuscript received June 18, 2007; revised January, 30 2008. This work was supported by the Engineering Research Centers Program, National Science Foundation, under Award EEC-9986866. The authors are with the Engineering Research Center for Wireless Integrated MicroSystems, Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI 48109-2122 USA. Digital Object Identifier: 10.1109/JPROC.2008.922564 1184 Proceedings of the IEEE | Vol. 96, No. 7, July 2008 0018-9219/$25.00 Ó2008 IEEE Authorized licensed use limited to: National University of Singapore. Downloaded on October 6, 2008 at 6:15 from IEEE Xplore. Restrictions apply.
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INV ITEDP A P E R
Microelectrodes,Microelectronics, andImplantable NeuralMicrosystemsProgress in development of tiny electrodes, cables, circuitry, signal processors
and wireless interfaces promises to advance understanding of the
human nervous system and its disorders.
ByKensall D. Wise, Life Fellow IEEE, Amir M. Sodagar, Member IEEE, Ying Yao, Member IEEE,
Mayurachat Ning Gulari, Gayatri E. Perlin, and Khalil Najafi, Fellow IEEE
ABSTRACT | Lithographically definedmicroelectrode arrays now
permit high-density recording and stimulation in the brain and
are facilitating new insights into the organization and function of
the central nervous system. They will soon allow more detailed
mapping of neural structures than has ever before been possible,
and capabilities for highly localized drug-delivery are being
added for treating disorders such as severe epilepsy. For chronic
neuroscience and neuroprosthesis applications, the arrays are
being used in implantable microsystems that provide embedded
signal processing and wireless data transmission to the outside
world. A 64-channel microsystem amplifies the neural signals by
60 dB with a user-programmable bandwidth and an input-
referred noise level of 8 �Vrms before processing the signals
digitally. The channels can be scanned at a rate of 62.5 kS/s, and
signals above a user-specified biphasic threshold are transmitted
wirelessly to the external world at 2 Mbps. Individual channels
can also be digitized and viewed externally at high resolution to
examine spike waveforms. The microsystem dissipates 14.14 mW
amplifiers provide closed-loop gains of 1000, equivalent
input noise levels of 8 �Vrms, an upper cutoff frequency of
10 kHz, and lower cutoffs below 100 Hz [62]. Program-
mable low-frequency cutoffs have recently been demon-strated [64] to include or reject slow-wave activity,
depending on the application. State-of-the-art amplifiers
[45] dissipate 75 �W from �1.5 V and fit in an area of
0.07 mm2 in 0.5 �m complementary metal–oxide–
semiconductor (CMOS). Recognizing that most sites will
not be near neurons of interest, a front-end site selector
allows the user to choose from a large number of sites on
Fig. 14. Block diagram of a simple multichannel neural recording system.
Fig. 15. Functional block diagram of a 32-channel digital spike
detector [76].
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the probe (electronic site positioning). The external probe
interface then typically requires seven leads (three power,
data in, data out, clock, and strobe), independent of the
number of sites. For a stimulating probe, the desired
current amplitudes and site addresses are entered serially,
and the generated currents are steered to the sites through
Fig. 16. Functional block diagram of the signal processor [77].
Fig. 17. The formation of data packets by time-division multiplexing the spike detector outputs.
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the site selector while providing recording access to anysite [46], [65]. Microsystems using both active (multi-
plexed and nonmultiplexed) and passive electrode arrays
have been reported, but as these systems evolve, more will
likely incorporate on-chip circuitry to reduce lead counts
and improve system reliability.
B. Neural Signal ProcessorsFig. 13 shows a block diagram of the complete
microsystem required for wireless cortical recording,
either for neuroscience or for neuroprosthesis applica-
tions. After amplification of the neural signals, here using16- or 64-channel amplifier chips housed in the
electronics package, the signals are fed to a neural signal
processor (NPU). In its simplest form, such a processor
could be realized by a simple time-domain analog
multiplexer as in Fig. 14. This approach keeps the system
complexity low as long as the number of recording
channels is small. For instance, the three-channel analog
recording system reported in [66] easily fits on a 2.2 �2.2 mm2 chip in 1.5 �m CMOS technology. As soon as the
number of channels grows, however, the amount of neural
Fig. 18. (a) Time-domain data compression in an eight-channel spike detector module by using a local memory and (b) forming the
outgoing data packets out of the neural activity retrieved from four such modules.
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information to be wirelessly transmitted will increase tosuch an extent that it cannot be easily handled. Thus, the
signal processor in Fig. 13 must take a more active role in
processing and compressing the recorded data. Much of
the emphasis in future generations of implantable
microsystems will focus on improving both the quantity
and quality of neural recordings. Work on the former will
focus primarily on increasing the number of channels,
while quality will focus on preserving as many features ofthe neural signal as possible, starting from the spike width
above a user-supplied threshold and progressing to a high-
resolution digital record of the spike waveform. In this
evolution, tradeoffs among system capability, power, and
size will be important. Sophisticated digital signal-
processing (DSP) algorithms for spike detection and
classification [67]–[73] require too much power and size at
present, so reported spike detectors have used user-programmable thresholds to provide information on spike
occurrence. This is adequate for some applications [74], [75]
and significantly reduces the amount of transmitted data.
A simplified block diagram of a mixed-signal neural
processor [76] is shown in Fig. 15. It receives four time-
multiplexed analog signal channels, each containing the
neural signals recorded on eight sites. After 5-bit analog-to-
digital conversion, the resulting signal amplitudes areseparated into 32 individual channels. A special-purpose
digital spike processor then computes the signal averages
and standard deviations for the individual channels and
calculates appropriate biphasic thresholds based on a user-
specified number of standard deviations. The amplitude of
each channel is then compared to its threshold to detectneural spikes. If the channel is active (contains a spike), the
sample is tagged with the associated channel address and
put in a buffer to be transmitted wirelessly at 2.5 Mbps.
This NPU provides full waveform information on neural
spikes above threshold, ignores subthreshold noise, saves a
factor of about 12 in output bandwidth, and increases the
number of allowable channels from 25 to 312.
The 32-channel signal processor [77] shown in Fig. 16supports two operational modes.
• In Scan Mode, all neural channels are scanned for
the occurrence of neural spikes. The addresses of
the active channels, i.e., channels with above-
threshold neural activity, are sorted, packed, and
sent to the outside world through a reverse
(outgoing) telemetry link. The threshold polarity
(positive, negative, or biphasic) and its level are setby the user for each channel individually.
• In Monitor Mode, a neural channel is selected,
sampled at high resolution, and transmitted to the
outside world.
This processor is equipped with additional circuitry
that allows it to be used as a 32-channel module in a 64-
channel neural processing architecture. Intrachip modu-larity (implemented by using eight-channel spike detectormodules (SD-8) in parallel) helps achieve high channel
scan rates. The number of channels handled by each
module, the number of parallel modules, and the size of
the local memory on each module are parameters that
can be optimized according to the targeted recording
Fig. 19. Data packaging [80]: (a) Incoming data packet, (b) outgoing data packet in Scan Mode, and (c) outgoing data packet in Monitor Mode.
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capacity and speed. Interchip modularity allows expansionof the system by simply configuring one processor as the
master and another one (or more) processor(s) as the
slave(s).
C. Data CompressionPerhaps the simplest scheme for sending the detected
neural activity to the outside world is to use time-division
multiplexing, as illustrated in Fig. 17. The binary outputsof all the spike detectors are periodically scanned and
sent to the external interface after data packet formation
[78]. However, since the extracellularly recorded action
potentials from cortical neurons have typical durations of
approximately 1 ms with firing rates from G 1 to 150
spikes per second [79], the output of each spike detector
contains useful information (neural activity) only a small
Fig. 20. Wireless transfer of data packets along with a synchronized clock [80]: (a) functional block diagram and
(b) actual operating waveforms.
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portion of time. Hence, transmitting the outputs of all thespike detectors (independent of their contents) wastes
significant recording bandwidth. A more efficient ap-
proach is to transmit only spike activity information
from active channels [77]. Here, when a spike is
detected on a given channel, the address of that channel
is sent to the external host. As shown in Fig. 16, the
local memory in each SD-8 module temporarily stores
the active channel addresses. The memory space is sharedamong all eight channels. The data fusion core polls the
four SD-8 modules to fetch the active channel addresses,
completing the process of spike detection, channel-
address tagging, spike sorting, and spike queuing. The
addresses retrieved from the four parallel queues will then
be used to make an outgoing data packet, as illustrated in
Fig. 18.
D. Bidirectional Wireless InterfacesImplantable recording microsystems need to be
powered and programmed through a forward telemetry
link in order to perform long-term wireless recording via a
reverse wireless path, as shown in Fig. 13. To support
these requirements, a major building block in such
systems is a bidirectional wireless interface that contains
at least two parts:/ a forward telemetry front-end that retrieves an
incoming stream of data packets containing setup
commands and data and provides the microsystem
with regulated supply voltages derived from the
modulated inductively coupled radio-frequency
carrier.
/ a reverse telemetry back-end that prepares the out-
going stream of neural data packets, and transmitsthe recorded information to the external world.
E. Data Transfer and PackagingWireless digital data transfer between the implantable
microsystem and the external host is basically asynchro-
nous, so to simplify detection and minimize power, a
synchronized clock is embedded in the data stream. To
facilitate recognition of a data packet and help the receiverseparate it from the preceding one, a start pulse (usually a
predefined pattern of 0s and 1s) is sent ahead of each
packet, and parity bits are added to each packet to allow
error detection. Fig. 19 shows incoming and outgoing data
packets [80], designed to program the microsystem and
transmit the recorded neural information, respectively. In
the incoming data packet, B0 and B1 encode four
commands, and B2–B9 carry the channel address ofinterest for either spike detection setting or for Monitor
Mode operation, or to convey required settings to the
channel of interest for operation in Scan Mode. The
outgoing data packet format in Scan Mode reports the
detected neural activity on one channel per each SD-8
using four bits. Every 8 bits are accompanied by a parity
bit. In Monitor Mode, each data packet carries two
consecutive 8-bit amplitude samples, along with two paritybits. The chip address bit (CAB), seen in the outgoing data
packet in both modes, is used when two 32-channel neural
processors of the same type (shown in Fig. 16) are
connected in a master–slave configuration to realize a 64-
channel processor. The CAB represents the chip that has
Fig. 21. Wireless recording in Monitor Mode.
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prepared the data packet (B1[ for the master chip and B0[for the slave).
Fig. 20 presents the functional details of the reverse
telemetry link. First, the data packets are Manchester
encoded in order to incorporate the system clock. Then,
the encoded bit stream is on–off key (OOK) modulated
and is wirelessly transmitted to the external system. On
the external receiver side, after amplification, the reverse
process consisting of OOK demodulation and Manchesterdecoding is performed in order to retrieve the received
data and a synchronized clock.
F. Wireless RecordingWireless implantable recording microsystems have
recently been demonstrated [78], [80]. Fig. 21 shows in-
vivo recordings obtained in Monitor Mode from the guinea
pig auditory cortex using one of these systems [80]. TheBoriginal signal[ here is the amplified neural signal before
analog-to-digital conversion on the microsystem side, and
the Breconstructed signal[ is the retrieved signal after
passing through the wireless link, external receiver, and
postprocessing software in the host computer (see Fig. 13).
The signals shown in Fig. 21 are a small slice of a 30-s
recording in which two separate units were identified
based on postrecording analysis. For the first unit, thesignal/noise ratio (SNR) decreased from 11.21 on the
system side to 8.77 after recovery on the external side;
the second unit experienced an SNR reduction from 4.56
to 3.35 in passing through the same path. This degradation
in the signal quality comes from analog/digital conversion
with 8-bit resolution. Wireless data transfer does not
contribute to SNR reduction because digital data
modulation/transfer is used. Averaged over 24 886 datapackets, the packet error rate was 0.33%, which is excellent
performance for wireless data transfer in this application.
For a 2 MHz clock, the channel scan rate for spike
detection in this system is 62.5 kS/s and the total system
power dissipation at 1.8 V is 14.4 mW. The implantable
version of the microsystem measures 1.4 � 1.55 cm2,fitting on a U.S. penny.
IV. CONCLUSIONS
The technology for creating neural probes capable of
acutely measuring the neuronal activity throughout a
volume of tissue is now in place. Using three-dimensional
arrays of stimulating and recording electrodes, detailedmapping of connections in the nervous system should soon
be possible. Such studies will give important insights into
the signal-processing techniques used in the nervous
system and into the disorders that disrupt them. For
chronic studies, further advances are needed in the
electrode–tissue interface, and these needs are driving
electrode size toward cellular dimensions and below.
Beyond the electrodes themselves, substantial progresshas been made in the cables, site-selection and amplifica-
tion circuitry, embedded neural signal processors, and
wireless interfaces needed for chronic investigations in
neuroscience and for neural prostheses. The first com-
pletely implantable neural microsystems are now emerging
and should stimulate substantial progress in both areas. The
coming decade should see some dramatic breakthroughs in
our understanding of the nervous system and in our abilityto treat its disorders. h
Acknowledgment
The authors would like to thank Dr. F. Terry Hambrecht
and Dr. W. J. Heetderks of the Neural Prosthesis Program,
National Institute of Neurological Disorders and Stroke, for
their vision and support of this work over many years, aswell as the many faculty, students, and staff at the
University of Michigan who contributed to this research
in so many important ways. The support and encourage-
ment provided by P. V. Anderson of Cupertino, CA, is also
gratefully acknowledged.
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[46] M. D. Gingerich, J. F. Hetke, D. J. Anderson,and K. D. Wise, BA 256-site 3D CMOSmicroelectrode array for multipointstimulation and recording in the centralnervous system,[ in Dig. Int. Conf. Solid-StateSens. Actuators (Transducers’01), Munich,Germany, Jun. 2001.
[47] Q. Bai and K. D. Wise, BA high-yieldmicroassembly structure forthree-dimensional microelectrode arrays,[IEEE Trans. Biomed. Eng., pp. 281–289,Mar. 2000.
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[49] X. Cui, V. A. Lee, Y. Raphael, J. A. Wiler,J. F. Hetke, D. J. Anderson, and D. C. Martin,BSurface modification of neural recordingelectrodes with conducting polymer/biomolecule blends,[ J. Biomed. Mater. Res.,vol. 56, pp. 261–272, 2001.
[50] R. Biran, D. C. Martin, and P. A. Tresco,BNeuronal cell loss accompanies the braintissue response to chronically implantedsilicon microelectrode arrays,[ Exp.Neurology, Jul. 2005.
[51] Y. Yao, M. N. Gulari, S. Ghimire, J. F. Hetke,and K. D. Wise, BA low-profile three-dimensional silicon/parylene stimulatingelectrode array for neural prosthesisapplications,[ in Dig. IEEE Conf. Eng. Med.Biol., Shanghai, China, Sep. 2005.
[52] J. Chen, K. D. Wise, J. F. Hetke, andS. C. Bledsoe, Jr., BA multichannel neuralprobe for selective chemical delivery at thecellular level,[ IEEE Trans. Biomed. Eng.,vol. 44, pp. 760–769, Aug. 1997.
[53] D. Papageorgiou, S. C. Bledsoe, M. Gulari,J. F. Hetke, D. J. Anderson, and K. D. Wise,BA shuttered probe with in-line flowmetersfor chronic in-vivo drug delivery,[ in Dig.IEEE Microelectromech. Syst. Conf., Interlaken,Jan. 2001, pp. 212–215.
[54] D. Papageorgiou, S. Shore, S. Bledsoe, andK. D. Wise, BA shuttered probe with in-lineflowmeters for chronic in-vivo drug delivery,[IEEE J. Microelectromech. Syst., vol. 15,pp. 1025–1033, Aug. 2006.
[55] Y. Li, K. Baek, M. Gulari, and K. D. Wise, BAdrug-delivery probe with an in-line flowmeterbased on trench refill and chemicalmechanical polishing techniques,[ in IEEESensors Conf., Atlanta, GA, Oct. 2007.
[56] K. Baek, Y. Li, M. N. Gulari, and K. D. Wise,BA pneumatically-actuated microvalve forspatially-selective chemical delivery,[ in Dig.North Amer. Conf. Solid-State Sens., Actuators,Microsyst., Hilton Head, SC, Jun. 2006,pp. 155–158.
[57] Y. Li, BAn integrated drug-delivery probe withan in-line flowmeter,[ Ph.D. dissertation,Univ. of Michigan, Ann Arbor, 2006.
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[59] J. Wang, M. N. Gulari, and K. D. Wise, BAparylene-silicon cochlear electrode array withintegrated position sensors,[ in Dig. IEEE Int.Conf. Eng. Med. Biol., New York, Sep. 2006,pp. 3170–3173.
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[61] Q. Bai and K. D. Wise, BSingle-unit recordingwith active microelectrode arrays,[ IEEETrans. Biomed. Eng., pp. 911–920, Aug. 2001.
[62] R. H. Olsson, III, M. N. Gulari, andK. D. Wise, BA fully-integrated bandpassamplifier for extracellular neural recording,[in Proc. 1st Int. IEEE EMBS Conf. Neural Eng.,Capri, Italy, Mar. 2003, pp. 165–168.
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Wise et al. : Microelectrodes, Microelectronics, and Implantable Neural Microsystems
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ABOUT THE AUTHORS
Kensall D. Wise (Life Fellow, IEEE) received the
B.S.E.E. degree (with highest distinction) from
Purdue University, West Lafayette, IN, in 1963
and the M.S. and Ph.D. degrees in electrical
engineering from Stanford University, Stanford,
CA, in 1964 and 1969, respectively.
From 1963 to 1965 and from 1972 to 1974, he
was a Member of Technical Staff with Bell
Telephone Laboratories, where his work was
concerned with the exploratory development of
integrated electronics for use in telephone communications. From 1965
to 1972, he was a Research Assistant and then a Research Associate and
Lecturer in the Department of Electrical Engineering, Stanford, working
on the development of micromachined solid-state sensors. In 1974, he
joined the Department of Electrical Engineering and Computer Science,
University of Michigan, Ann Arbor, where he is now the J. Reid and Polly
Anderson Professor of Manufacturing Technology and Director of the
Engineering Research Center for Wireless Integrated MicroSystems. His
present research focuses on the development of integrated microsys-
tems for health care and environmental monitoring. In 2002, he was
named the William Gould Dow Distinguished University Professor at the
University of Michigan, where he also held the 2007 Henry Russel
Lectureship.
Dr. Wise is a member of the U.S. National Academy of Engineering. He
organized and was the first Chairman of the Technical Subcommittee on
Solid-State Sensors of the IEEE Electron Devices Society (EDS). He was
General Chairman of the 1984 IEEE Solid-State Sensor Conference, IEEE-
EDS National Lecturer (1986), and Technical Program Chairman (1985)
and General Chairman (1997) of the IEEE International Conference on
Solid-State Sensors and Actuators. He received the Paul Rappaport
Award from the EDS (1990), a Distinguished Faculty Achievement Award
from the University of Michigan (1995), the Columbus Prize from the
Christopher Columbus Fellowship Foundation (1996), the SRC Aristotle
Award (1997), and the 1999 IEEE Solid-State Circuits Field Award.
Amir M. Sodagar (Member, IEEE) received the
B.S. degree from K. N. Toosi University of Tech-
nology (KNTU), Tehran, Iran, in 1992 and the M.S.
and Ph.D. degrees from Iran University of Science
and Technology (IUST), Tehran, in 1995 and 2000,
respectively, all in electrical engineering.
He was with S. Rajaee University as a Lecturer
from 1995 to 2000, with Iran Telecommunication
Research Center (ITRC) as a Design Engineer
during 1996–1997, with VLSI Circuits and Systems
Laboratory, University of Tehran, as a Research Engineer from 1997 to
1998, and with EMAD Semicon as a Senior Design Engineer from 1998 to
2000. He was a Guest Lecturer at several institutions during this time and
was a member of the Electrical and Electronics Engineering Technical
Committee of the International Kharazmi Youth Innovation Festival
during 1995–2000. In 2000, he joined the National Science Foundation
Engineering Research Center for Wireless Integrated MicroSystems
(WIMS), University of Michigan, Ann Arbor, as a Research Fellow, where
he worked on integrated microsystems for electric nerve stimulation. In
2002, he joined KNTU as an Assistant Professor. Since 2004, he has been
with WIMS as a Visiting Associate Research Scientist and subsequently as
the Technical Director for Biomedical Microsystems. His research
interests are in mixed-signal integrated circuits and biomedical implant-
able microsystems. He is the author of Analysis of Bipolar and CMOS
Amplifiers (Boca Raton, FL: CRC Press/Taylor & Francis Group, 2007).
Ying Yao (Member, IEEE) received the B.S.
degree in electrical engineering from Wuhan
University, China, in 1997 and the M.S. and Ph.D.
degrees in electrical engineering from the Univer-
sity of Michigan, Ann Arbor, in 2000 and 2005,
respectively.
Since 1999, she has been a Research Assistant
and then a Research Fellow with the Engineering
Research Center for Wireless Integrated Micro-
Systems, University of Michigan. Her research
interests focus on the development of implantable wireless integrated
microsystems for neural stimulating and recording with applications in
brain–machine interfaces, neuroscience, and neuroprostheses.
Wise et al. : Microelectrodes, Microelectronics, and Implantable Neural Microsystems
Vol. 96, No. 7, July 2008 | Proceedings of the IEEE 1201
Authorized licensed use limited to: National University of Singapore. Downloaded on October 6, 2008 at 6:15 from IEEE Xplore. Restrictions apply.
Mayurachat Ning Gulari received the B.S. degree
in chemical technology and the M.S. degree in
polymer science and engineering from Petroleum
and Petrochemical College, Chulalongkorn Uni-
versity, Bangkok, Thailand, in 1993 and 1995,
respectively.
She was a Planning Engineer with Star Petro-
leum Refining Company (a joint venture of
Chevron and Texaco), where she worked on
optimization of feedstock and products. She is
currently a Research Engineer in the Engineering Research Center for
Wireless Integrated MicroSystems, University of Michigan, Ann Arbor,
where her research activities involve designing and fabricating micro-
machined silicon probes for drug delivery, recording, and stimulation.
Gayatri E. Perlin received the B.S.E. and M.S.E.
degrees in electrical engineering from the Univer-
sity of Michigan, Ann Arbor, in 2001 and 2003,
respectively, where she is currently pursuing the
Ph.D. degree in electrical engineering.
Her thesis is focused on the development of a
fully implantable microsystem for neural prosthe-
ses. Her research interests include microfabrica-
tion, microelectromechanical devices, and
integrated circuits for biomedical and other
applications.
Khalil Najafi (Fellow, IEEE) received the B.S., M.S.,
and Ph.D. degrees from the Department of Elec-
trical Engineering and Computer Science, Univer-
sity of Michigan, Ann Arbor, in 1980, 1981, and
1986, respectively, all in electrical engineering.
He is the Schlumberger Professor of Engineer-
ing in the Electrical Engineering and Computer
Science Department, University of Michigan. He
was Director of the Solid-State Electronics Labo-
ratory from 1998 to 2005. He has been Director of
National Science Foundation (NSF)’s National Nanotechnology Infra-
structure Network (NNIN) since 2004 and Deputy Director of the NSF
Engineering Research Center on Wireless Integrated Microsystems
(WIMS) at the University of Michigan. His research interests include
micromachining technologies, micromachined sensors, actuators, and
microelectromechanical systems; analog integrated circuits; implantable
biomedical microsystems; micropackaging; and low-power wireless
sensing/actuating systems. He has been active in the field of solid-state
sensors and actuators for more than 20 years. He has been involved in
several conferences and workshops dealing with microsensors, actua-
tors, and microsystems, including the International Conference on Solid-
State Sensors and Actuators, the Hilton Head Solid-State Sensors and
Actuators Workshop, and the IEEE/ASME Micro Electromechanical
Systems Conference. He is an Associate Editor of the Journal of
Micromechanics and Microengineering and an Editor for Sensors and
Materials.
Dr. Najafi is a Fellow of AIBME. He received a National Science
Foundation Young Investigator Award from 1992 to 1997. He is an
Associate Editor of the IEEE JOURNAL OF MICROELECTROMECHANICAL
SYSTEMS. He was an Associate Editor of the IEEE JOURNAL OF SOLID-
STATE CIRCUITS from 2000 to 2004, the Editor for Solid-State Sensors of
the IEEE TRANSACTIONS ON ELECTRON DEVICES from 1996 to 2006, and an
Associate Editor of the IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
from 1999 to 2000.
Wise et al. : Microelectrodes, Microelectronics, and Implantable Neural Microsystems
1202 Proceedings of the IEEE | Vol. 96, No. 7, July 2008
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