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Hardware Design for Realization of Smart Sensor Network
A Proposal to the Army Research Laboratory PEER Program
UMCP Researchers: Professor Martin Peckerar and Professor Neil Goldsman, Dept. of Electrical and Computer
Engineering
ARL Researchers: Mr. Luke Currano, Dr. Nibir Dhar and Dr. Madan Dubey
I. Background
Advances in micro-electrical-mechanical systems (MEMS), coupled with new optical devices, present enormous
possibilities in the areas of sensors and energy harvesting. Marrying the MEMs and optical sensors (such as IR
detectors) with smart electronics can also open new doors in novel machine vision technology such as neuromorphic
spectrally agile and retinal sensors. Further, nano-retina sensor concepts could benefit significantly by the combination
of new electronic interfaces/protocols that are likely to evolve through this effort. In view of the above thoughts, sensor
interface ICs (in the form of ASICs, microcontrollers, DSP, FPGA and/or ADC) can play a vital role in making sensors
smarter, and to work effectively in a particular application by amplifying, pre-processing, filtering, and correcting the
sensor's signal; eliminating non-linearities and compensating for environmental factors (such as temperature or
humidity) that can impair the sensor's measurement accuracy (particularly for PZT MEMs sensors), thereby
significantly reducing sensor-signal post-processing requirements, and facilitating the sharing of sensor data with the
host back-plane processors.
The Army Research Laboratory has a leading program on the development of new MEMS structures, and IR sensors.
These state of the art sensors, actuators and energy harvesters are now ready to be integrated into systems where their
benefits can be realized. Using these new devices, we plan to design and fabricate complex distributed smart sensor
networks. As a long term goal, we propose to develop a distributed network of small smart sensors that can monitor the
environment, communicate with each other, locate sensed objects, perform distributed computations, and reach joint
decisions by fusing the data the individual sensors acquire. These sensors could be placed in small UAVs or UGVs for
an ad-hoc and adaptive sensor network. Each of the network nodes will include several MEMS and or optical sensors,
one or two microprocessors, a transceiver, and perhaps an energy harvester. We expect there to be anywhere from ten
to hundreds and perhaps thousands of nodes in the network including UGS. The connectivity of the network will
depend on the transmitting power and the distance between nodes.
Figure 1a illustrates the structure of a single node containing one or more MEMS/optical sensors, an analog to digital
interface, a processor for interpreting the sensor data and controlling the network, and then a transceiver to share and
receive the sensor information with the outside world. Figure 1b illustrates the ad-hoc sensor network. While our long
term goals are to develop sophisticated ad-hoc wireless smart sensor networks that may include new sensor modalities
that are the results of multidisciplinary technologies such as MEMs/IR, our short term goals are to develop the
electronics necessary to control the MEMS and optical sensor devices, extract the sensed information, and where
appropriate transform it into digital signals for micro-processing. In the long-term however, we hope to develop onsensor signal processing capabilities that communicates intelligent and relevant information rather than simple images
and sensory data.
Figure 1a: Smart Sensor Node;
Network
Figure 1b: Smart Sensor
II. Impact of Proposed Research
We have well developed sensor networks and VLSI design groups at the ECE Dept. of the University of
Maryland. Our program in sensor networks is aimed at developing millimeter scale smart sensor nodes called smart
dust. With this program, we are designing our own analog and digital integrated circuits and fabricating them through
the MOSIS facility. The circuits are for sensing, communication and digital control. This program has a funding level
of more than $2 million over the next 4 years. We also have a program on the development of 3-dimensional integrated
circuits, which is funded at the $1 million level. Both of these programs fit very well with the goals of our proposed
work on electronics for MEMS and Optical Sensor networks with the Army Research Lab. The ARL will be able to
benefit significantly from our past experience with these programs by leveraging many of the techniques that have
been uncovered. Addition of this project will increase the scope of investigation and lay foundation for next
generation of electronics that will enable not only smart sensor network capability, but will also offer circuit concepts
for new sensor modalities and new sensor materials/structures such as PZT/ceramic MEMS devices, nanosensor
electronic interface and 3D electronics to develop integrated light weight heterogeneous sensors for deploying in small
military platforms with smart communication protocols.
III. Research Program: Approach & Milestones
Year I Tasks and Milestones
The focus of our research program in the first year will be to work closely with the MEMS and Infrared Optical
Detector groups at ARL to develop state of the art electronics hardware to interface and control their unique sensors.
For the MEMS group it is important to develop the electronics to interface their unique devices with the outside world.
In particular, we plan to begin our collaboration with the design and development of electronics to help realize the
capabilities of (a) IR Optical Sensor Arrays, including intelligent night vision and smart read capabilities; (b) MEMS
devices including the Shock Sensor; Piezoelectric Vibrational Energy Reclamator; and RF Switches & Phase Shifters.
The performance and problems of Piezoelectric Ceramic devices are quite different from silicon and other
semiconductor devices. Therefore, new device design rules for component architectures will be developed.
All classes of circuits described above will be designed, fabricated, tested and integrated into the sensor/system
configuration under development at each phase of the program. Each year, the resulting circuit will be optimized for
performance in their chosen application realm.
III.a Circuit Development Activity
High Dynamic Range Readouts in the Presence of Large Background Signals for Optical Sensor Arrays. The main
issue here is the cancellation of the large background signal current. This is a well-known problem in infrared optical
sensing, where the thermal backgrounds are enormous. It is necessary to store, or “memorize,” a time-integrated DC
current from the “sky” (or some background region not collecting data from the region of interest), and to subtract that
current from the sensor output when the sensor is switched to the actual scene. There are two approaches in general
use. The first is known as “charge skimming” in which an active current source is used to withdraw (or “skim”) a fixed
amount of current from a readout node. The second approach requires the “storage” of a background current to be
subtracted from the total current generated by a sensor trained on a target scene. We have chosen the latter as our
approach of choice as it offers the possibility of feedback stabilization and low noise.
We plan to follow the approach found in (IEEE Journal of Solid State Circuits, vol. 29, no. 10,1288(1994)). Consider
the following circuit:
Fig. 2 The basic “current memorization” circuit.
The both the sensor background and scene sensing signals are transduced to voltages that are impressed on the M act
gate. This sets the current through the M1,M2 source. During the background measurement, the gate of M sw is high.
This effectively stores the background current, Imem, through the voltage on Cs. During the scene sensing phase, the
bias on the Mact gate changes. The background is still passed to the Vss supply, but the extra signal current is forced on
to C0. Thus, only the scene-minus-background signals are passed to the output. All currents are feedback stabilized
through the tandem source, M3 and M4. The resulting circuit is compact enough to be used as a single readout in a
high-density array. This circuit is ideal for imaging array applications for wavelengths into the IR.
IIIb. Analog to Digital Conversion with Range Compression for MEMS and Optical Sensors: The signals coming from
the MEMS and optical devices will be analog. However, it is often convenient to process theses signal digitally. We
therefore plan to develop analog-to-digital converters for this purpose. The second circuit employed is a novel
“companding” (ADC). This circuit is based on the standard “flash” design, but the ladder resistances are scaled to
perform any degree of scale compression. The compander is fairly linear at low signal inputs, but it achieves any level
of desired compression at high signal input levels. This creates an enormous increase in dynamic range of the ADC.
We have actually fabricated this circuit and have demonstrated its high-speed performance (>100MHz) using the
MOSIS AMI 0.5 micron process. The basic architecture and chip layout are shown in Figure 3:
Figure 2. “Companding” flash ADC. The resistor “weights” are
set to create any desired compression function.
IIIc. Voltage and Current Controllers for RF MEMS Switches & Phase Shifters: We plan to sense the change in
capacitance and resonant frequency of the piezoelectric devices. This will be achieved by comparing the signal
response of the piezoelectric device to a known reference. Our plan is to use ultra-stable band-gap referenced supplies
to create the drive voltages.
IIId. Control Circuits for MEMS devices: In addition to ADC circuits, we will also provide control circuits for MEMS
devices. For example, the ARL MEMS group is interested in controlling their MEMS Shock Sensor. This sensor closes
a circuit when a critical acceleration is reached. To detect the closed circuit, we will apply a voltage to the
accelerometer terminal which in turn will source a signal to a transimpedance amplifier which will provide a trigger
signal to a comparator threshold detector circuit. This comparator will then activate a signal to a current source which
will provide the energy to the thermal actuator to release the latch.
IIIe. Circuits for Energy Harvesting: To store energy harvested using the Piezoelectric MEMS vibrational energy
reclamator we will use a full bridge rectifying circuit in series with an energy storage capacitor. Our circuit will be
calibrated to the output impedance of the reclamator.
Year 2: We will develop our own sensor nodes using a combination of existing IC’s, and ones that we build and
design at ARL and UMCP. The control and sense electronics will be developed using CMOS technology, designed
with Cadence, and fabricated through MOSIS. Physical interfaces between MEMS and Optical sensors using standard
bonding methods.
Year 3: The nodes will be assembles into planar, multi-chip modules. The multi-chip modules will be prototyped
using a 100-micron resolution pick and place machine. The nodes will be programmed and the sensor network will be
established.
Years 4 & 5: We will transform the planar multi-chip module smart sensor nodes into tiny wireless smart
sensor nodes using 3D integration of integrated circuits. The 3D integration will be achieved by first
designing and having our own chips fabricated through MOSIS. The chips will be specially designed for 3D
implementation, requiring development of new 3D design rules and establishing of specific symmetries for
3D interconnects between chips. 3D interconnects will be facilitated by first thinning the SOI CMOS chips.
We have engaged in similar process in collaboration with MIT Lincoln Laboratories.
Annual Budget:
Graduate Research Assistant focusing on device simulation:
½ Graduate Research Assistant focusing on thermal simulation:
Faculty salary guiding overall program:
45K
23K
27K
Total:
95K