JOURNAL OF MICROELECTROMECHANICAL SYSTEMS, VOL. 22, NO. 3, JUNE 2013
779
Integrated Multifunctional Environmental Sensors
Clifton L. Roozeboom, Matthew A. Hopcroft, Wesley S. Smith, Joo Yong Sim, Student Member, IEEE,
David A. Wickeraad, Peter G. Hartwell, and Beth L. Pruitt, Member, IEEE, Member, ASME
Abstract—We present the design, microfabrication, and characterization of ten sensors on one silicon die. We demonstrate
simultaneous monitoring of multiple environmental parameters,
including temperature, humidity, light intensity, pressure, wind
speed, wind direction, magnetic field, and acceleration in three
axes. Through an integrated design and fabrication process, these
ten functions require only six photolithography mask steps. Temperature is measured redundantly using aluminum and doped
silicon resistance thermal detectors and a bandgap temperature
sensor. Humidity is transduced by the dielectric change of a
polymer due to water absorption. Light intensity is measured
with a p-n junction photodiode and doped silicon photoresistor.
Pressure is transduced using piezoresistor strain gauges on a
sealed membrane. Wind speed and direction are measured with
two perpendicular hot wire anemometers. Magnetic field strength
is measured with a doped Hall effect sensor. Acceleration in three
axes is measured using electrostatic comb finger accelerometers,
and an additional z-axis accelerometer uses piezoresistor strain
gauges. We measured the cross-sensitivity of each function to
all other environmental parameters and can use the chip’s multifunctional capabilities to compensate for these effects. Sensor
integration can enable significant cost, size, and power savings
over ten individual devices and facilitate deployment in novel
applications.
[2012-0155]
Index Terms—Electrostatic devices, fabrication, micromachining, piezoresistive devices, sensor fusion, sensor systems.
I. I NTRODUCTION
T
HIS paper expands on an earlier report of our multifunctional integrated sensors (M-FISes) and provides details
on the integrated design, analysis, and fabrication required
Manuscript received June 11, 2012; revised December 12, 2012; accepted
January 21, 2013. Date of publication March 22, 2013; date of current version
May 29, 2013. This work was supported in part by a Hewlett-Packard Laboratories Innovation Research Program Grant, in part by a Hewlett-Packard research
and education gift grant, and in part by the NSF Center of Integrated Nanosystems Grant ECCS-083281. Additionally, individual authors were supported by
the Stanford Mechanical Engineering Department Fellowship (CR), the BioX
Fellowship at Stanford (JYS), and the Ilju Foundation Scholarship (JYS). Work
was performed in part at the Stanford Nanofabrication Facility (a member of
the National Nanotechnology Infrastructure Network), which is supported by
National Science Foundation Grant ECS-9731293, its laboratory members, and
the industrial members of the Stanford Center for Integrated Systems. Subject
Editor C. Hierold.
C. L. Roozeboom, J. Y. Sim, and B. L. Pruitt are with the Department of
Mechanical Engineering, Stanford University, Stanford, CA 94305-3030 USA
(e-mail: clifton@stanford.edu; simba85@stanford.edu; pruitt@stanford.edu).
M. A. Hopcroft, W. S. Smith, and P. G. Hartwell are with Hewlett-Packard
Laboratories, Palo Alto, CA 94304 USA (e-mail: hopcroft@hp.com).
D. A. Wickeraad is with the Department of Electrical Engineering, Stanford
University, Stanford, CA 94305-3030 USA, and also with Hewlett-Packard
Laboratories, Palo Alto, CA 94304 USA (e-mail: david.wickeraad@hp.com).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/JMEMS.2013.2245400
to combine ten sensing functions on a single silicon die [1].
Just a decade ago, advances in sensor hardware and communication technology enabled a “Smart Dust” paradigm where
networks of mobile sensor nodes were envisioned to collect
data about the physical world [2], [3]. However, achieving
“Smart Dust” networks will require a reduction in the size,
power, and especially the cost of the sensing nodes. Reducing
process complexity is key to reducing manufacturing costs [4],
while shrinking the footprint of multiple sensors eases integration in space-constrained platforms such as mobile phones
[5] and makes nodes easier to deploy [6]. Minimizing power
consumption enables longer term operation of battery-powered
nodes and moves in the direction of self-powered nodes as
energy harvesting methods improve [7]. Lowering the cost of
the nodes ultimately will enable sensor deployment in largescale networks and applications in consumer technology [6].
In the past decade, wireless sensor networks (WSNs) have
been reported for applications ranging from structural health
monitoring [8], to habitat and environmental monitoring [9],
and even parking space occupancy [10]. Advances in wireless
communications, data storage, and analytics will enable the use
of WSNs in a wider range of applications and will change how
we interact with the world. For example, Hewlett-Packard’s
vision for a Central Nervous System for the Earth unites sensors
with communications, cloud data management, and analysis to
share and manage spatially and temporally rich environmental
information [11].
Microelectromechanical systems (MEMS) are widely used
in WSNs, and advances in their manufacturing, materials, and
design are reducing individual sensor size, power, and cost.
An opportunity for additional savings lies in the integration
of multiple sensing functions onto a single MEMS die. By
comparison, the integrated circuit (IC) industry has benefited
from integration to reduce the size, cost, and power of ICs
while increasing their functionality, while the sensor community has not yet widely applied 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 multiparameter information.
However, researchers have demonstrated multimodal sensing
where redundant measurements benefit the application, e.g.,
chemical sensing. Hagleitner et al. demonstrated a single
chip that combined mass-sensitive, capacitive, calorimetric,
and temperature transducers for multiparameter gas chemical
analysis [12]. Boisen et al. used piezoresistive atomic force microscope probes as reconfigurable sensing platforms [13], and
Lee and Lee have shown a combined temperature and humidity
sensor [14]. Here, we present sensor fusion based on the combination of diverse functions in an integrated fabrication process.
1057-7157/$31.00 © 2013 IEEE
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TABLE I
S ENSOR F UNCTIONS AND T RANSDUCTION P RINCIPLES . T HE F IGURE
L ETTERS R EFER TO THE L ABELS ON THE S ENSOR D IE IN FIG. 1
Fig. 1. M-FISes sensor test die is 10 × 10 mm and includes ten sensing
functions detailed in Table III.
Six photolithography steps are used to define MEMS sensors
based on resistive, piezoresistive, and capacitive transduction as
well as the photoeffect and temperature effects of p-n junctions
(see Figs. 1 and 2). The resulting M-FISes chip comprises ten
sensors on a 10 × 10 mm die. M-FISes measures temperature,
humidity, light intensity, pressure, air speed, airflow direction,
magnetic field strength, and accelerations in three axes. We
achieve this integration by balancing minimum requirements
for transducer performance against fabrication complexity. The
cost, size, and power savings of a single integrated sensor
and application-specific IC can be substantial when compared
to ten comparable sensors and their measurement circuits. In
addition, we can employ the multifunctional information to
compensate for any cross-sensitivities that each sensor function
has. For example, the air speed sensor exhibits a significant
cross-sensitivity to humidity. Using the humidity sensor and a
measurement of the cross-sensitivity, we can compensate for
the effect in the measurement software. In Section II, we outline
the integrated fabrication process developed to combine the
varied sensing modalities and highlight the tradeoffs required
to ensure the functionality of all the sensors. In Section III,
we detail the fabrication process. In Section IV, we review
the methods of transduction, operating principles, and measurement circuits for each sensor and present calibration and
performance data for each function. Finally, in Section V, we
discuss the functionality and performance of M-FISes as a
whole and present cross-sensitivity data.
II. I NTEGRATED FABRICATION P ROCESS D ESIGN
The design of the M-FISes sensors and fabrication process
was guided by two competing goals: 1) to integrate the sensor
functions required for a generically useful environmental sensor, which provides the added benefits of cross-sensitivity compensation and 2) to minimize the complexity of the fabrication
process to demonstrate the potential for low-cost mass production. Ten sensor functions were targeted to provide the capabilities of a “weather center” with motion or activity sensing.
To integrate the ten sensor functions in a single fabrication
process, we focused on well-developed transduction mechanisms. We balanced competing design and fabrication param-
eters such that each sensor would provide useful measurement
performance in a “weather-center” scenario, i.e., with sufficient
resolution, dynamic range, and bandwidth for typical atmospheric conditions found on Earth. We identified a combination of four transduction mechanisms which could achieve the
desired sensor functions and performance: resistive, piezoresistive, capacitive, and p-n junction (diode). We then developed
a combined surface and bulk micromachining process using
silicon-on-insulator (SOI) wafers to implement these transduction mechanisms while minimizing the number of mask steps
in the process.
The final M-FISes process used six photolithography steps,
plus a manual polymer deposition step, for ten sensor functions
which represents significant reduction of process complexity
versus individual fabrication of each sensor. However, the
design parameters for an optimal fabrication process for each
sensor type vary widely and are frequently in opposition to
one another. Table II summarizes the trends in the performance
of each sensor with process parameter specifications. For the
transduction principles chosen for M-FISes, a thicker device
layer is beneficial to the humidity sensor and the x- and y-axis
accelerometers because a thicker device layer increases the area
for the capacitive comb finger structures which increases the
amplitude of the sensor output. For the accelerometers, a thicker
device layer also is beneficial for the x- and y-axis sensors
because of increased stiffness in the out-of-plane direction
and thus lower cross-axis sensitivity and detrimental to the
z-axis sensor because the sensing axis is stiffened. We chose an
8.5-μm device layer as a compromise between maximizing
z-axis sensitivity and minimizing x- and y-axis crosssensitivity. A thicker device layer also increases the stiffness
of the pressure sensor membrane, but we can independently
change the membrane length and width to achieve our desired
membrane stiffness.
High resistivity of the device layer (low background doping)
leads to better piezoresistor and diode performance because
ROOZEBOOM et al.: INTEGRATED MULTIFUNCTIONAL ENVIRONMENTAL SENSORS
781
TABLE II
E FFECT OF I NCREASING THE D ESIGN PARAMETER ON P ERFORMANCE OF I NDIVIDUAL S ENSOR F UNCTIONS.
(+) I MPROVED. (−) W ORSENED. (◦) I NTERMEDIATE L EVEL I S O PTIMAL
of less charge leakage and higher breakdown voltages from
the doped regions to the wafer substrate but decreases the
performance of capacitive devices because of decreased charge
mobility and charge spreading. Using Sze’s work relating carrier concentration and avalanche breakdown in abrupt junctions
[15], we chose a nominal background carrier concentration of
2 × 1017 atoms/cm3 for a breakdown voltage of approximately
5 V as our doping specification.
The optimal doping level for piezoresistive sensing regions
has been explored previously [16]–[19] and tends toward higher
doping levels (∼1020 atoms/cm3 ) to achieve lower Johnson
and Hooge noise while trading off the higher piezoresistive
coefficients and sensitivities achieved at low doping levels
(∼1016 atoms/cm3 ). Using the optimization procedure previously explored in our laboratory, we set a nominal target of
8.6 × 1018 atoms/cm3 [17]. Deep junctions are detrimental to
piezoresistor sensitivity because bending stress decreases as
you move toward the neutral axis of a material. However,
deeper junctions are beneficial for photodiode sensitivity because longer wavelength radiation can be absorbed. We designed the fabrication process with a nominal final junction
depth of 0.36 μm as sufficient for both piezoresistor and photodiode operation.
The surface-micromachined thin-film sensors (metal resistance thermal detectors (RTDs) and anemometers) are largely
unaffected by the parameters discussed.
III. FABRICATION P ROCESS
Variations of the M-FISes designs were fabricated on 4-in
(100) SOI wafers (8.5-μm device layer, 1-μm buried oxide
(BOX), and 500-μm handle wafer). Fig. 2 and Table III show
the fabrication process for four sensor functions that are representative of the process parameters. The p-type device layer had
a starting resistivity of 5 Ω · cm. We increased the background
doping to a resistivity of 0.2 Ω · cm using BBr3 gas diffusion
and annealed the wafers for 24 h in 1100 ◦ C in a nitrogen
ambient furnace.
Alignment marks were patterned in the silicon [see Fig. 2(i)],
and 25 nm of screening oxide was grown by thermal oxidation.
The thermal oxide was patterned and etched to open windows
Fig. 2. Cross section of fabrication process showing the aluminum RTD,
pressure sensor, x-axis accelerometer, and humidity sensor. The integrated
process requires six photolithography mask steps.
for diffusion doping [see Fig. 2(ii)]. The piezoresistors and
other doped structures were formed by n-type diffusion in
POCl3 gas at 800 ◦ C for 40 min. We chose n-type diffusion
rather than p-type diffusion because of better control of doping
levels and junction depth in our processing facilities. The
screening oxide and borosilicate glass formed during diffusion
were etched in a buffered oxide etch (BOE) solution (34%
NH4 F, 7% HF, and 59% water). The wafers were thermally oxidized again to grow a 1-μm-thick oxide that was patterned and
etched in BOE [see Fig. 2(iii)]. The thermal oxide acts as a passivation layer for the piezoresistors and electrical isolation from
the substrate for the anemometers, bandgap temperature sensor,
Hall effect sensor, and metal RTDs. After high-temperature
processing, TSUPREM4 simulation modeling showed that
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TABLE III
P ROCESS F LOW L ISTING THE S ENSORS T HAT A RE
M ODIFIED D URING E ACH FABRICATION S TEP
Fig. 3. Four-wire measurement setups for (a) RTDs and (b) bandgap temperature sensors eliminate the impedance contributions of the wiring so that only
the sensor resistance is measured. We use an LM334 as the current source.
IV. D ESIGN AND C ALIBRATION
M-FISes devices were fabricated on a 10 mm × 10 mm
silicon die, as shown in Fig. 1, with the sensors identified
in Table I. The following sections present the principle of
operation, design, and proof-of-concept data for each function.
Each sensor was first tested individually while keeping other
measurement parameters constant and then tested for crossparameter sensitivity. We present data from different design and
fabrication variations with repeated testing of multiple chips
when possible. The sensor tests were performed in ambient
conditions of 25 ◦ C, approximately 35% relative humidity, and
atmospheric pressure unless otherwise noted.
A. Temperature
the junction depth and peak carrier concentration should be
0.36 μm and 8.6 × 1018 cm−3 . Next, a 1-μm layer of aluminum
was sputtered onto the surface, patterned, and plasma etched
using a chlorine-based chemistry [see Fig. 2(iv)]. The device
layer was then patterned and etched from the front side using
deep reactive ion etching (DRIE) to define the geometry for the
accelerometers and humidity sensor [see Fig. 2(v)]. Trenches
10 μm wide were etched through the device layer to the BOX
to electrically isolate each sensor. The front sides of the device
wafers were temporarily bonded to a backing wafer using
Crystalbond (Structure Probe, Inc., West Chester, PA, USA).
The backing wafer served as a structural and protective layer
during backside DRIE to pattern the pressure sensor membrane
and etch underneath the anemometers and accelerometers. The
BOX layer was then plasma etched from the backside to release
the anemometers, accelerometers, and pressure sensor cavity
[see Fig. 2(vi)]. The backing wafer was removed by soaking in
a water bath at 70 ◦ C for 30 min and then gently sliding apart
the two wafers.
We used a laser saw to score and then cleave the wafers to
separate the sensor die. The pressure and humidity sensors required postprocessing steps performed on the individual die [see
Fig. 2(vii)]. The pressure sensor reference cavity was sealed at
room temperature and ambient pressure by epoxy bonding the
backside of the sensor chip to a ceramic package, and then, the
chip was wire bonded. Finally, for proof-of-concept testing,
SPR220 photoresist (Shipley Company, LLC, Marlborough,
MA, USA) was manually applied to the humidity sensor comb
fingers with a syringe. The sensor die was heated on a hot plate
to 70 ◦ C for 10 min to evaporate the photoresist solvents.
Temperature sensors exist in many forms from commercial
RTDs, thermistors, and thermocouples [20] to sensors based on
bipolar transistors [21] and MEMS resonators [22]. M-FISes
was designed with five different temperature sensors to test
a variety of sensor designs. The doped silicon and aluminum
RTDs transduce a change in temperature (T ) to a change in
resistance by the relation
T = Tref +
ΔR
Rref
α
(1)
where α is the thermal coefficient of resistance, Rref and Tref
are the reference resistance and temperature, and ΔR is the
change in sensor resistance. For aluminum, αAl is approximately 0.0037 C−1 at room temperature [23], while for doped
silicon, αSi is a function of dopant type and concentration [24],
[25]. The α value of a material is itself a function of temperature
which leads to increasing nonlinearity with a larger measurement range. For the M-FISes design, aluminum RTDs with a
nominal resistance of 5 kΩ and 25 Ω and doped silicon RTDs
with a nominal resistance of 5 kΩ and 100 Ω were fabricated to
compare sensor designs and provide redundant measurements.
In a bipolar transistor, the emitter–base voltage changes with
temperature [26]. We use this principle to fabricate a bandgap
temperature sensor that uses the temperature dependence of the
forward voltage drop of a p-n diode (VBE ) to convert a temperature change to a voltage change. By comparing the voltage
drop of two p-n diodes at different bias currents (Ibias ), the nontemperature-related dependences of VBE can be negated, and
the governing equation simplifies to
Ibias1
kB T
∗ ln
VBE1 − VBE2 =
(2)
e
Ibias2
ROOZEBOOM et al.: INTEGRATED MULTIFUNCTIONAL ENVIRONMENTAL SENSORS
783
Fig. 4. Temperature calibration data for (a) bandgap temperature sensor at 1- and 10-mA diode biases, (b) 100-Ω doped RTD at 10-mA bias, (c) 5-kΩ doped
RTD at 0.25-mA bias, (d) 25-Ω aluminum RTD at 10-mA bias, and (e) 5-kΩ aluminum RTD at 1-mA bias.
where kB is Boltzmann’s constant and e is the fundamental
charge of an electron.
The temperature sensors were calibrated using an oven
(Blue M) to control temperature. The voltage drop across the
sensors was measured using a four-wire setup with separate
current source and voltage measurement wires to eliminate the
impedance contribution of the wiring and contact resistances
(see Fig. 3). The relationship between temperature and the
output voltage of the five sensor variations is shown in Fig. 4.
The experimentally measured αAl was 0.0034 C−1 , and αSi was
0.0015 C−1 which approximately agree with the experiments by
McCurry [23] and Norton and Brandt [24].
B. Humidity Sensor
Humidity sensors based on polymer thin films are commercially available, and researchers have demonstrated a variety of
resistive and capacitive sensor designs [27], [28]. In M-FISes,
humidity is transduced by a change in the dielectric constant
of a polymer layer as it absorbs and desorbs moisture from
ambient air. The dielectric constant of water is roughly 78 at
room temperature [29] and is much higher than that of most
polymers, causing a large change in capacitance upon water
absorption [30]. The dielectric constant of the polymer with
absorbed water is
3
1
1
1
3
3
3
− εpolymer
(3)
+ εpolymer
ε = γ εH2O
where γ is the volume fraction of water in the film and εpolymer
and εH2O are the relative dielectric constants of the polymer
and water, respectively [30]. The polymer is applied between
the electrodes of an interdigitated comb finger capacitor where
the relationship between the dielectric constant of the polymer
and the sensor capacitance is
C=
nεo εl w
d
(4)
where n is the number of capacitor fingers, l is the length
of the capacitor finger, w is the thickness of the capacitor
finger, d is the gap between capacitor fingers, and εo is the
permittivity of free space. Dai demonstrated the capabilities
of this sensor design in a surface-micromachined process with
stacks of metal and insulator layers to form the electrodes
and a polyimide dielectric layer [31]. In this paper, we use
a bulk micromachining process to form electrodes from the
bulk silicon and demonstrate the functionality using SPR220
photoresist as the polymer layer (see Fig. 5). We apply 5 μL of
Fig. 5. Humidity sensor (a) diagram and (b) optical micrograph show the
interdigitated comb finger electrodes and photoresist layer that form the sensor
capacitor. The dielectric constant of the photoresist changes with humidity
which causes a capacitance change.
the photoresist by hand using a pipette and then heat the sensor
die to remove the solvents.
We measured the sensor capacitance using a HewlettPackard (HP) 4284A LCR meter and conducted calibration
testing in an Atlas Ci3000+ Weather-Ometer, holding the temperature constant at 25 ◦ C while varying humidity (see Fig. 6).
We measured a time constant of 11 min for a step input
from 86 percent relative humidity (%RH) to 25%RH for the
sensor shown. The output of the device tested is nonlinear with
increasing sensitivity at increasing humidity levels, possibly
due to water condensing on the sensor and electrodes.
C. Light Sensor
Ambient light intensity is measured in two ways, using a
p-n junction photodiode and a photoresistor. The photodiode
operates in the photovoltaic mode where the light-generated
current IL from the device is governed by
IL =
eηPopt
hv
(5)
where e is the fundamental charge, η is the efficiency, Popt is
the incident optical power, and hν is the photon energy [15].
The photoresistor operates on the principle that light striking
doped silicon will excite bound carriers from the valence to
the conduction band thus increasing the conductivity of the
semiconductor.
The photodiode operates in a transimpedance amplifier circuit, employing an OP27 operational amplifier with a gain of
1 V/1 μA, to convert the diode current to an output voltage [see Fig. 7(a)]. We measured a sheet resistance of
111 Ω/square for the sensor under test which agrees with simulation results. We performed light intensity calibration using
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Fig. 6. (a) Humidity sensor time series data at 25 ◦ C illustrate measurement functionality and transient response to a step input change to humidity. (b) Calibration
curve shows that sensitivity is nonlinear and increases as relative humidity increases. (c) Humidity sensor response to a step input from 86%RH to 25%RH shows
a time constant of 11 min.
Fig. 7. (a) Photodiode and transimpedance amplifier circuit using an OP27 op amp with gain of 1 V/1 μA for sensor testing. (b) Photoresistor measurement
circuit uses a Wheatstone bridge at 1-V bias and INA110 instrumentation amplifier to perform the resistive measurement. The 1-Hz low-pass circuit at the input of
the amplifier attenuates high frequency noise. (c) Calibration data for the photodiode and photoresistor show that the high gain in the photodiode transimpedance
amplifier gives high sensitivity at low light levels but causes the amplifier to saturate at high light levels. The photoresistor and interface circuit provide a larger
measurement range.
Fig. 8. (a) Pressure sensor uses four piezoresistors connected in a Wheatstone bridge and amplifier circuit using an INA110 instrumentation amplifier and 1-Hz
low-pass filter. (b) Optical micrograph shows a pressure sensor with an 8.5-μm-thick membrane sealed at ambient temperature and pressure by epoxy bonding the
M-FISes chip to a PLCC package. (c) Pressure sensor calibration shows good linearity with a sensitivity of 16 mV/kPa at a bias of 1 V.
an incandescent microscope light source and an Avago
APDS-9007 ambient light sensor to measure illuminance [see
Fig. 7(c)].
The photoresistor is a meandering trace of p-type doped
silicon with a dark resistance of 7.7 kΩ for the device tested.
The photoresistor Rlight was placed in one leg of an off-chip
Wheatstone bridge, and the bridge output was zeroed with the
photoresistor in the dark [see Fig. 7(b)]. The Wheatstone bridge
was biased at 1 V, and the output from each side of the bridge
fed into an INA110 instrumentation amplifier with a gain of
500 and a 1-Hz low-pass filter. As tested, the photodiode and
amplifier provide a high sensitivity at low light levels but lead
to the saturation of the op amp at higher light intensities. The
photoresistor can operate at higher light intensity levels which
extends the measurement range of the chip. For the photodiode,
the gain of the amplifier circuit can also be reduced to extend
the measurement range.
D. Pressure Sensor
Pressure sensors are perhaps the most ubiquitous MEMS
sensors and generally rely on membrane deflection to cause
a change in resistance, capacitance, or optical properties [32].
The M-FISes pressure sensor consists of a single crystal silicon
membrane with four piezoresistive strain gauges at the edges
of the membrane, two oriented in the longitudinal direction and
two in the transverse direction (see Fig. 8) [33]. The piezoresistors are rotated 45◦ with respect to the wafer flat because
the 100 directions have the highest piezoresistance coefficient
in n-type doped silicon [18]. The change in resistance of the
longitudinal and transverse piezoresistors is governed by
ΔR
= σ l πl
(6)
R l
ΔR
= σ t πt
(7)
R t
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Fig. 9. (a) Anemometer circuit diagram uses a Wheatstone bridge and OP213 op amp to convert the sensor resistance to a voltage output across the readout
resistor Rro . (b) SEM image of the anemometer shows the 1000-μm-long suspended aluminum wire over the etched silicon cavity. (c) Anemometer calibration
shows a straight line sensitivity of 150 mV/m/s. (d) Differential output from the x and y anemometers shows that the relative magnitude of heat transfer from the
anemometer varies with the airflow direction over the chip.
where ΔR/R is the relative change of resistance of the piezoresistor, σ is the average stress in the respective direction of
the piezoresistor, and π is the piezoresistive coefficient in the
direction of stress. For a square membrane, the maximum stress
at the edge of the pressure membrane is approximately
σ=
0.308P l2
t2
(8)
where P is the pressure, l is the length of the side of the membrane, and t is the membrane thickness [34]. The four piezoresistors are initially balanced and measured in a full Wheatstone
bridge circuit so that, for nominally identical piezoresistors
with small changes in resistance, the bridge output voltage
Vbridge is approximately given by
ΔR
Vbridge
ΔR
1
ΔR
(9)
≈
−
≈
Vbias
2
R l
R t
R
where Vbias is the bridge supply voltage. The voltage output of
the piezoresistor Wheatstone bridge is connected to an INA110
instrumentation amplifier with a gain of 500 and a 1-Hz lowpass filter [see Fig. 8(a)]. The sensor was tested in a vacuum
chamber from ambient to 67 kPa at room temperature and had
a sensitivity of 16 mV/kPa at a bias of 1 V [see Fig. 8(c)].
E. Anemometer
Microscale devices are used in a range of fluid sensing and
flow control research [35] with innovations such as a 3-D
anemometer with joints that fold out of the sensor plane [36]
and direction sensors with angular resolution of 4◦ [37]. For
M-FISes, the air speed and direction sensors consist of two hot
wire anemometers positioned perpendicular to each other so
that a flow rate and direction vector can be calculated from the
sensor readings [see Fig. 9(b)]. The anemometer wires are patterned in the 1-μm-thick aluminum layer, and the surrounding
silicon is etched away in the front and backside DRIE steps to
reduce conductive heat transfer to the sensor die. Various wire
lengths and widths were fabricated with data presented for a
1000 × 2 μm sensor.
Hot wire anemometers are essentially long suspended metal
resistors that are placed in the fluid flow. They transduce the
fluid velocity from a change in the resistance of the wire due
to convective heat transfer from the wire to the bulk fluid. The
relation between input electrical power and heat loss is
I 2 Rw = havg Aw (Tw − Tf )
(10)
where I is the current through the hot wire, Rw is the wire resistance, havg is the average convective heat transfer coefficient,
Aw is the wire surface area, Tw is the temperature of the wire,
and Tf is the temperature of the fluid. The local heat flux q at
any point on the anemometer wire may be expressed as
q = hlocal (Tw − Tf )
(11)
where hlocal is the local convection coefficient. When the
anemometer is oriented perpendicular to the air stream, we can
estimate q and hlocal to be constant due to the small cross
section of the wire. As the wire is rotated parallel to the stream
direction, there is an increasing component of airflow parallel
to the wire. As fluid flows along the wire, a thermal boundary
layer develops because the free stream and surface temperatures
differ. For this parallel stream, the increasing thermal boundary
causes a decreasing local convective coefficient and decreasing
heat flux [38]. With two perpendicular anemometers exposed to
the fluid flow, a relative measure of the stream direction can be
obtained by taking the arctangent of the two outputs. However,
this method corresponds to four possible flow vectors that are
90◦ apart. To find two possible vectors at 180◦ of separation, a
system of three or more anemometers can be used, or to find a
unique vector, an array of temperature sensors around a central
heater has been demonstrated [39]. Adding this capability in
future designs would not add fabrication complexity.
The M-FISes anemometers are exposed to the environment
so that air can flow over the surface of the packaged chip. The
anemometers are each connected in a Wheatstone bridge with
an operational amplifier and are operated in the constant temperature mode [see Fig. 9(a)]. An OP213 operational amplifier
supplies the bridge excitation current, and the output voltage
is measured across the readout resistor Rro . The individual
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Fig. 10. SEM image of an x-axis accelerometer and inset of the interdigitated comb finger electrodes show the geometry of the sensor with structural
elements fabricated by bulk etching the silicon substrate. The comb finger electrodes are unequally spaced (inset) so that displacement from the equilibrium
position will cause an overall capacitance change in the electrode. Electrodes 1
and 2 are the positive sensor outputs, electrodes 3 and 4 are the negative outputs,
and electrode 5 or 6 is used for the input signal.
Fig. 11. Block diagram of the charge-to-voltage converter shows how a
change in capacitance in the x- and y-axis accelerometers is converted into
a voltage signal. An oscillating voltage is applied to the proof mass of the
accelerometer, and the signal is measured on the stationary electrodes. The
differential capacitance change and high frequency carrier form two opposite
signals that are detected and amplified in symmetrical circuits. The signals are
demodulated to remove the high frequency carrier. The instrumentation amplifier rejects common mode signals from the accelerometer and amplifies the differential signal to provide an analog voltage output proportional to acceleration.
anemometers were calibrated using a custom-built wind box
with a variable-speed fan and a Sper Scientific 840030 rotary
vane anemometer [see Fig. 9(c)]. We varied the direction of the
airflow in 22.5◦ increments and measured the differential output
of the anemometers to show that the difference roughly follows
a sinusoid as expected [see Fig. 9(d)]. Asymmetry in the data is
probably due to drift over the duration of the test.
F. x- and y-Axis Capacitive Accelerometers
A wealth of research and publications exist on MEMS-based
accelerometers [40], and commercial MEMS-based devices
have been available for decades [41]. Chau et al. demonstrated
a classic structural design with integrated MOS circuitry similar to the proof mass that we designed [42]. We measure
x- and y-axis accelerations with two individual sensors that
detect the inertial force imposed on a suspended proof mass
in the x- or y-direction. The inertial force causes a change
in displacement of the proof mass which causes a capacitance change of the interdigitated comb finger electrodes. The
x- and y-axis accelerometers are identical in design and oriented orthogonal to each other on the sensor die. Capacitance
changes as the gap between comb fingers changes due to acceleration. Approximating the comb fingers as individual parallel
plate capacitors, the change in capacitance, ΔC, is
ΔC = −
nεo εair lwΔd
do (do + Δd)
(12)
where n is the number of comb fingers, εo is the permittivity
of free space, εair is the relative dielectric constant of air, l and
w are the dimensions of the comb finger, do is the initial gap
between comb fingers, and Δd is the change in comb finger
gap [34]. The gap change is related to acceleration by the mass
and spring constant of the suspension structure. More detailed
solutions for electrostatic comb fingers can be found in [43]
and [44].
The accelerometer proof mass, springs, and comb finger
electrodes are formed by bulk etching the SOI device layer (see
Fig. 10). Aluminum bond pads provide electrical connection to
the proof mass and stationary electrodes. There are four sets
of stationary electrodes that are electrically isolated from each
Fig. 12. Circuit diagram of the charge-to-voltage converter shows the op
amps and components selected. The output of the accelerometer is connected
to a transimpedance amplifier using an LF356 op amp. The transimpedance
amplifier acts as the current detector in the block diagram image. The variable
capacitor Cf is used to balance the negative and positive outputs of the measurement circuit. The demodulator circuit is a diode and RC circuit with a time
constant of 100 Hz which filters out the high frequency carrier and allows low
frequency acceleration signals to pass through unattenuated. The demodulated
signal feeds into an INA103 instrumentation amplifier with a gain of 1000.
other. The comb finger electrodes are unequally spaced so that
displacement from the equilibrium position will cause a change
in capacitance. The capacitive electrodes are designed so that
electrodes 1 and 2 change positively and electrodes 3 and 4
change negatively when the proof mass moves to the left. Electrodes 5 and 6 are used to bias the proof mass electrodes. The
differential capacitance design provides first-order rejection of
the common mode signal. The nominal accelerometer capacitance is 0.3 pF, the nominal capacitance change is 10 fF/g,
and the natural frequency is 1.4 kHz.
To measure the differential capacitance change, we designed
a charge-to-voltage converter (see Fig. 11) from discrete operational amplifiers and circuit components (see Fig. 12). A high
frequency carrier signal at 400 kHz is applied to the accelerometer proof mass. The positive and negative accelerometer outputs are connected to two separate current detectors that convert
the sensor capacitance to a current and, then, a voltage signal.
ROOZEBOOM et al.: INTEGRATED MULTIFUNCTIONAL ENVIRONMENTAL SENSORS
787
Fig. 13. (a) x- and y-axis accelerometer calibration data with error bars for
ten tests. (b) Time series data for y-axis accelerometer with an applied external
oscillation at two different amplitudes. The frequency of oscillation is 1 Hz.
The accelerometer sensitivity is 1.9 V/g at a carrier amplitude of 1 V.
The accelerometer transduces the low frequency acceleration
into an amplitude modulation of the carrier frequency. The
output from the current detector is a mixed signal of the low
frequency acceleration and the high frequency carrier signals.
The mixed signal is demodulated using a diode rectifier and RC
circuit. We set the cutoff frequency to 100 Hz to filter out the
high frequency carrier signal. We can change the resistor and
capacitor values to vary the cutoff frequency and change the
accelerometer bandwidth depending on performance needs and
sensing application. The instrumentation amplifier differences
and amplifies the demodulated positive and negative signals to
provide an analog voltage output proportional to acceleration.
The specific components and circuit design were based on
a differential capacitance-to-voltage converter demonstrated by
Lotters et al. [45]. Ignoring nonidealities in the sensor and
measurement circuit and assuming small sensor capacitance
changes, the simplified transfer function for the measurement
circuit is
Vout = Hina (V+ − V− )
2ΔC
Vout = − Hina Vcar
Cf
(13)
(14)
Fig. 14. (a) Microscope images of the capacitive z-axis accelerometer show
the proof mass and spring design and multiple bond pads on the device for
different configurations of wire bonds to the package. (b) SEM image of the
interdigitated electrodes at one corner of the cantilever proof mass shows the
comb fingers bowing out of plane. The bowing leads to reduced performance
and variable sensitivity of the accelerometer.
Fig. 15. Charge-to-voltage converter for the capacitive z-axis accelerometer
operates like half of the x- and y-axis circuit. We input a carrier frequency of
100 kHz into the proof mass of the accelerometer. The stationary electrodes are
connected to a transimpedance amplifier that converts the change in charge to a
voltage signal. The high frequency carrier is filtered by the demodulator circuit
and the signal is amplified using an inverting amplifier with a programmable
reference voltage.
where Hina is the gain in the instrumentation amplifier, Vcar is
the amplitude of the carrier signal, and Cf is the feedback capacitor in the current detector. The calibration curve in Fig. 13
shows the accelerometer and measurement circuit output from
−1 to 1 g, and the time series data show the sensor oscillating
at 1 Hz at two different magnitudes.
G. z-Axis Capacitive Accelerometer
The z-axis capacitive accelerometer uses the change in
overlap between capacitive comb finger electrodes to convert
the proof mass displacement to a capacitance change. The
governing equation for the z-axis accelerometer is
ΔC = −
nεo eair lΔw
d
(15)
where the electrode overlap changes, Δw, as the proof mass
moves into or out of the plane of the sensor die. Like the x- and
y-axis accelerometers, the proof mass, springs, and electrodes
are formed by bulk etching the device layer silicon.
Four cantilever springs support the accelerometer proof mass
in a pinwheel-type layout [see Fig. 14(a)]. Using COMSOL
3.0 for finite-element analysis, each cantilever support has a
Fig. 16. (a) Capacitive z-axis accelerometer calibration data with error bars
for eight tests show different sensitivities for positive and negative accelerations. (b) Oscillation at 1-Hz frequency at two amplitudes illustrates the
dynamic response of the accelerometer.
stiffness of 4 N/m, and the device has a natural frequency of
750 Hz. The cantilever structure is 140 times stiffer in the x- and
y-axes than in the sensing axis. Examining the proof mass in an
electron microscope at a 45◦ viewing angle, we can see that the
interdigitated electrodes are bowed out of the plane of the die
and there is not a consistent overlap along the perimeter of the
proof mass [see Fig. 14(b)]. Possible reasons for the bowing
are intrinsic stress in the device layer of the SOI wafer or
surface stress from the metal layer on top of the proof mass. The
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Fig. 17. (a) Microscope image of the piezoresistive z-axis accelerometer shows the proof mass with two piezoresistive strain gauges and two adjacent matched
resistors. The matched on-chip resistors are used for first-order temperature compensation in the measurement circuit. (b) Measurement circuit uses the four
on-chip resistors in a Wheatstone bridge and INA110 instrumentation amplifier to amplify the bridge voltage. A 100-Hz low-pass filter at the input to the amplifier
filters high frequency noise. (c) Calibration data with error bars for ten tests show sensitivity of 70 mV/g at 1-V bias and nonlinearity of 1% of full scale output.
bowed electrodes reduce sensitivity for negative accelerations
[see Fig. 16(a)].
The measurement circuit for the z-axis accelerometer is
essentially half of the charge-to-voltage converter used for the
x- and y-axis accelerometers (see Fig. 15). A high frequency
carrier signal at 100 kHz is applied to the accelerometer proof
mass. The output of the accelerometer is connected to a current
detector that converts the change in charge of the accelerometer
into a current and, then, a voltage. The output signal from
the current detector is a mixed signal of the high frequency
carrier and low frequency acceleration. The mixed signal is
demodulated using a diode and RC voltage rectifier. The signal
is amplified using an inverting amplifier with 100 times gain
and adjustable voltage reference.
The accelerometer calibration measurements [see Fig. 16(a)
and (b)] show the nonlinearity and variability in the accelerometer sensitivity. The calibration curves for the z-axis
accelerometers exhibit different slopes for negative and positive
accelerations due to the out-of-plane bowing of the comb fingers.
H. z-Axis Piezoresistive Accelerometer
Roylance and Angell engineered one of the first MEMS accelerometers using a silicon cantilever beam and mass structure
[46]. M-FISes uses a similar accelerometer with two piezoresistors at the base of a cantilever and released proof mass structure
to convert acceleration in the z-axis to a change in resistance
[see Fig. 17(a)]. The approximate relation for ΔR/R is the
same as (5) for the pressure sensor, but the approximate stress
in the piezoresistor is
σmax =
Mt
2I
(16)
where M is the moment at the base of the cantilever caused by
the inertial force on the proof mass, t is the thickness of the
cantilever, and I is the moment of inertia of the cantilever. The
cantilever support beams are 500 μm long and 15 μm wide,
and the proof mass is approximately 1000 μm × 1000 μm.
The piezoresistor extends 10 μm along the cantilever from
the base. A 4-μm-wide trench is etched between the legs
of the piezoresistor to decrease the cantilever stiffness and
reduce leakage current across the legs of the piezoresistor,
resulting in a nominal natural frequency of 2.4 kHz.
As with many of the other resistive sensors, the accelerometer is wired in a Wheatstone bridge that feeds into an INA110
instrumentation amplifier [see Fig. 17(b)]. Two matched onchip piezoresistors adjacent to the strain gauges provide temperature compensation and first-order rejection of other common
mode signals. Calibration results are shown in Fig. 17(c).
The piezoresistive z-axis accelerometer provides much better
linearity and repeatability than the z-axis capacitive design and
occupies a much smaller area on the sensor die.
I. Magnetic Field
Micromechanical magnetometers employ a wide spectrum
of sensing principles, and Lenz and Edelstein provide a good
survey of the range of sensors [47]. The principle of the Hall
effect and the Lorentz force exerted on a charged particle
moving through a magnetic field is a simple way to make a
magnetometer without the need for magnetic materials. The
M-FISes magnetometer is a doped Van Der Pauw structure
with cloverleaf geometry which is commonly used to measure
the resistivity and Hall coefficient of semiconductor samples
[48]. We apply a bias current across opposite sections of the
cloverleaf from 1 to 3 [see Fig. 18(a)]. If a magnetic field
normal to the plane of the structure is present, the Lorentz force
will deflect the path of the charge carriers and will cause a
differential voltage across contacts 2 and 4. This is called the
Hall voltage VH and is governed by
VH =
Ibias B
eNe xj
(17)
where B is the magnetic field normal to the plane of the sensor,
e is the elementary charge, Ne is the magnitude of the electron
density, and xj is the junction depth. Sensor calibration was
conducted in a calibrated magnetic field generator at a 0.1-mA
bias current from −1 to 1 T [see Fig. 18(c)].
V. R ESULTS AND D ISCUSSION
The goal of the M-FISes design and fabrication was to
engineer a sensor that could simultaneously measure multiple
environmental parameters with sufficient sensitivity for ambient environmental changes. The straight line fit sensitivity,
resolution, and sensor power consumption for each function are
ROOZEBOOM et al.: INTEGRATED MULTIFUNCTIONAL ENVIRONMENTAL SENSORS
789
Fig. 18. (a) Diagram of the cloverleaf Van Der Pauw structure illustrates how the sensor is electrically wired. Bias current is applied between electrodes 1 and
3, and the Hall voltage which is proportional to magnetic field strength was measured between electrodes 2 and 4. (b) SEM image of the structure shows the
metal contacts connected to the doped cloverleaf geometry. The silicon between quadrants of the cloverleaf is etched away to the BOX layer. (c) Magnetometer
calibration at 0.1-mA bias current shows a sensitivity of 0.38 mV/T. The magnetic field was applied perpendicular to the sensor die.
TABLE IV
S TRAIGHT L INE F IT S ENSITIVITY OF C ALIBRATION DATA AND P OWER
D ISSIPATION FOR E ACH S ENSOR F UNCTION U SING THE T EST
C ONDITIONS D ESCRIBED IN SECTION IV
presented in Table IV. The power consumption data are for the
sensor only and do not include front end electronics. Resolution
was measured for a bandwidth of 0.01 to 100 Hz and includes
noise from the sensor and electronics. The resolution measurement for the humidity sensor was performed using a charge-tovoltage converter like the circuit used for the capacitive z-axis
accelerometer rather than the LCR meter used in the sensitivity
analysis. The resolution measurement for the magnetic field
sensor included an instrumentation amplifier to add output gain
that was not included in the sensor calibration.
We conducted cross-sensitivity analysis of the effect of
variations in temperature, humidity, light intensity, air speed,
pressure, magnetic field, and acceleration in the y- and
z-axes on each sensor function. We omitted changes in airflow
direction and x-axis acceleration because of redundancy and
omitted the bandgap temperature sensor because of the limited
number of bond pads on the sensor package. We made a cover
that we glued to the package that exposed the anemometers and
light sensors to the open environment and shielded the other
sensors from direct exposure to light, particles, and wind. The
cover was not sealed and still allowed the detection of environmental changes. Fig. 19 displays a matrix of graphs of the
cross-sensitivity data. The sensor functions are organized down
the rows and the measurands across the columns. Each graph
plots the change of the sensor output with respect to an initial
reference. The temperature, humidity, and pressure plots are
referenced to typical environmental conditions: temperature at
25 ◦ C, humidity at 30%RH, and pressure at 101 kPa. The light,
air speed, magnetic field, and acceleration tests are referenced
to zero, e.g., no applied magnetic field.
The largest cross-sensitivity of all the sensors except the capacitive accelerometers is to temperature. This is not surprising
since much of the design of a sensor and the measurement
electronics is focused on compensating for temperature effects.
The resistive (RTDs, photoresistor, and anemometer), piezoresistive (pressure sensor and accelerometer), and magnetic field
sensor exhibit significant and relatively linear sensitivity to
temperature due to the temperature coefficient of resistance of
the sensing material. The pressure sensor and accelerometer
were designed with on-chip Wheatstone bridges to reject the
common mode signal of temperature, but resistor mismatch
leads to the temperature dependence shown. The temperature
and humidity controlled oven was unable to maintain a set
point of 35%RH at low temperatures which is illustrated by the
reference humidity data in Fig. 19. As a result, the effects of the
temperature and humidity cross-sensitivities are compounded,
which is evident in the capacitive z-axis accelerometer graphs.
Relative humidity had a strong effect on the capacitive
accelerometer outputs due to the dielectric coefficient of air
being a strong function of humidity and the accelerometers
not being sealed from the ambient environment. The change
of offset corresponded to −16 g for the x- and y-axis devices
and −450 g for the z-axis device at 85%RH. By comparison,
the piezoresistive accelerometer has a much smaller sensitivity
to humidity. The mechanism of the piezoresistor sensitivity to
humidity is likely charge leakage due to moisture in the air and
moisture condensing on the chip.
The anemometer and humidity sensor were not shielded from
light by the sensor cover to allow for open access to environmental conditions. As a result, light intensity had a significant
effect on the anemometer and humidity offsets. Airflow over
the sensor changed the temperature of the chip as a whole and
thus affected all the functions. The temperature measured by the
metal and silicon RTDs decreased by almost 10 ◦ C for the 1 m/s
change in air velocity. An Analog Devices TMP36 temperature
sensor was used as a reference sensor for comparison. The
commercial sensor only changed about 2 ◦ C in the same airflow.
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Fig. 19. Matrix shows the output of (left) each sensor to (top) each measurand to provide a comparative measurement of the cross-sensitivities of each sensor
function to all possible measurands. Error bars for repeated trials are plotted for the temperature, humidity, and pressure tests but, in some cases, are too small
to be visible. The red triangular points are data from a reference temperature or humidity sensor in the temperature, humidity, air speed, and pressure tests. The
temperature, humidity, and pressure plots are referenced to typical environmental conditions: temperature at 25 ◦ C, humidity at 30%RH, and pressure at 101 kPa.
The light, air speed, magnetic field, and acceleration tests are referenced to zero, e.g., no applied magnetic field.
The difference in temperature change is probably due to the
plastic packaging of the commercial sensor. Change in pressure
slightly affected the humidity sensor and accelerometers, which
is expected. Magnetic field, in-plane acceleration (y-axis), and
out-of-plane acceleration (z-axis) had relatively small effects
on all the functions.
In addition to investigating the effect of temperature on the
offset of each function (TCO), we measured the temperature
effect on sensitivity for each function (see Fig. 20). In some
cases, the trend is clearly not linear, but we fit a slope to each
graph to find the temperature coefficient of sensitivity (TCS) as
a means to compare sensors. The TCS of the humidity sensor is
by far the largest among the sensors tested probably due to the
increased water absorption capability of the polymer sensing
layer at higher temperatures.
Humidity had a strong effect on the offset of the anemometer
and capacitive accelerometers, so we investigated the effect of
relative humidity on sensitivity (see Fig. 21). For the anemometer, sensitivity increased slightly as humidity increased and then
decreased by 28% at high humidity levels, probably due to
ROOZEBOOM et al.: INTEGRATED MULTIFUNCTIONAL ENVIRONMENTAL SENSORS
791
Fig. 20. Data of the change in sensitivity due to temperature for each sensor function are normalized to the sensitivity at 25 ◦ C (ΔS/SO ). A straight line fit of
the data approximates the TCS, although some of the trends are not clearly linear.
ACKNOWLEDGMENT
The authors would like to thank J. C. Doll and N. Harjee
for the discussions and assistance with fabrication and process
development.
Fig. 21. Anemometer and accelerometers displayed a significant change in
offset due to a change in humidity, so the effect of humidity on the sensitivities
of the sensors was investigated. The data were normalized by the sensitivity at
30%RH (ΔS/SO ).
water condensing on the wire. The trend in the x- and y-axis
accelerometers is unclear and may be a result of drift and noise.
The z-axis accelerometer shows a clear increase in sensitivity as
humidity increases, likely due to the higher dielectric constant
of air at higher humidity.
One inherent advantage of M-FISes is the ability to compensate for the cross-sensitivities that we measured. Knowing
the TCO and TCS of each sensor and having an accurate measurement of temperature, we can nullify the effect of temperature on all the sensors in our measurement software. Indeed,
most commercial sensors compensate for temperature, but with
M-FISes, we have a platform to measure and use computation
to compensate for ten different environmental parameters.
VI. C ONCLUSION
We have presented the highest degree of functional sensor integration yet demonstrated on a single MEMS device.
M-FISes has the capability of monitoring ten different parameters at once. The integrated fabrication process optimizes the
number of sensor functions and performance while minimizing
complexity and cost. The reduction of size, power, and cost is
important to advancing tools for WSNs and mobile platforms.
We have achieved stable operation of each sensor with the die
exposed to ambient conditions without special sealing methods.
We conducted a detailed investigation of the cross-sensitivities
of each of the sensor functions to each environmental parameter. We measured the TCS of all the sensors and the effect of
humidity on the sensitivity of the anemometer and capacitive
accelerometers. Cross-sensitivities between parameters exists
but can be compensated with measurement data from complementary sensors. Future research will focus on improving
the deployability and the long-term measurement capabilities
of M-FISes.
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Clifton L. Roozeboom received the B.S. degree
in mechanical engineering from The University of
Texas at Austin, TX, USA, in 2009 and the M.S. degree from Stanford University, Stanford, CA, USA,
in 2012, where he is currently working toward the
Ph.D. degree in mechanical engineering, focusing on
multifunctional self-powered sensing nodes.
Matthew A. Hopcroft received the B.Sc. degree in
computer engineering from The George Washington University, Washington, DC, USA, the M.Phil.
degree in engineering from Cambridge University,
Cambridge, U.K., and the Ph.D. degree in mechanical engineering from Stanford University, Stanford,
CA, USA.
He is a Researcher in the Information Infrastructure Laboratory at HP Laboratories, Palo Alto,
CA, USA, the central research organization for the
Hewlett-Packard Company. HP Laboratories is responsible for many technical achievements such as the pocket scientific calculator, thermal inkjet printing, and RISC computer architecture. He joined HP
Laboratories in 2010 as an ASEE/NSF Corporate Fellow. His research interests include microelectromechanical-systems design, microscale and portable
power systems, and sensor networks.
Wesley S. Smith received the B.S. degree in mechanical engineering from the University of California, Santa Barbara, CA, USA, in 2004 and the M.S.
and Ph.D. degrees in mechanical engineering from
Stanford University, Stanford, CA, USA, in 2006 and
2010, respectively.
At Stanford University, he focused on quantifying adhesion and friction forces of micro- and
nanoscale contacts of microelectromechanical systems (MEMS) probe arrays. He joined HP Laboratories, Palo Alto, CA, USA, in 2010, where he has
investigated advanced sensor networks and applications based on extremely
high performance MEMS accelerometers.
Joo Yong Sim (S’10) received the B.S. degree in
mechanical engineering from Seoul National University, Seoul, Korea, in 2008 and the M.S. degree
from Stanford University, Stanford, CA, USA, in
2010, where he is currently working toward the Ph.D.
degree in mechanical engineering, focusing on the
development of dynamic cell culture systems and
micromanipulation devices to study the mechanobiology of cell–cell adhesion and interactions.
ROOZEBOOM et al.: INTEGRATED MULTIFUNCTIONAL ENVIRONMENTAL SENSORS
David A. Wickeraad received the B.S. degree in
electrical engineering from the University of California, Los Angeles, CA, USA, in 2008 and the
M.S. degree in electrical engineering from Stanford
University, Stanford, CA, USA, in 2011.
Since 2008, he has been with Hewlett-Packard,
Palo Alto, CA, USA, designing and verifying ProVision application-specific integrated circuits for networking switches.
Peter G. Hartwell received the B.S.E. degree in
materials science from the University of Michigan,
Ann Arbor, MI, USA, and the Ph.D. degree in electrical engineering from Cornell University, Ithaca,
NY, USA.
He is currently a Distinguished Technologist at
Hewlett-Packard Laboratories, Palo Alto, CA, USA.
As a member of the Intelligent Infrastructure Laboratory, he is the Lead of the Central Nervous System
for the Earth (CeNSE) team developing a broad
sensing system to bring environmental awareness to
information technology infrastructure. CeNSE was selected as one of the 20
“World Changing Ideas” by Scientific American. He has extensive experience
in commercializing silicon microelectromechanical-systems (MEMS) products
and working on advanced sensors and actuators, and he specializes in MEMS
testing techniques.
793
Beth L. Pruitt received the B.S. degree from the
Massachusetts Institute of Technology, Cambridge,
MA, USA, in 1991 and the M.S. and Ph.D. degrees
from Stanford University, Stanford, CA, USA, in
1992 and 2002, respectively. During her Ph.D. work,
she developed piezoresistive cantilevers for characterizing thin-film gold electrical contacts.
In 2002, she worked on nanostencils and polymer microelectromechanical systems (MEMS) in the
Laboratory for Microsystems and Nanoengineering
at the Swiss Federal Institute of Technology (EPFL).
She joined the Mechanical Engineering faculty of Stanford University in Fall
2003 and started the Stanford Microsystems Laboratory. Her research interests
include piezoresistance, MEMS and manufacturing, micromechanical characterization techniques, biomechanics of mechanotransduction, the development
of processes, sensors, and actuators, and the analysis, design, and control of
integrated electromechanical systems. This research involves instrumenting
and interfacing devices between the micro- and macroscales, understanding
the scaling properties of physical and material processes, and finding ways
to reproduce and propagate new technologies efficiently and repeatably at the
macroscale. Prior to her Ph.D. at Stanford, she was an officer in the U.S.
Navy, at the engineering headquarters for nuclear programs, and a Systems
Engineering Instructor at the U.S. Naval Academy, where she also taught
offshore sailing.
Dr. Pruitt was the recipient of an NSF CAREER Award in 2005 and a DARPA
Young Faculty Award in 2009.