Download Hardware Design for Realization of Smart Sensor Network

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 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. These
issues will be accounted for in our circuit design for piezoelectric sensors which are described below.
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. Circuit Development Activity
IIIa. In a piezoelectric pressure sensor, a piezoelectric crystal is sandwiched between two conducting plates. Upon
deformation of the crystal a polarization will occur and a surface charge will be induced on the crystal. The charge can
then be transferred to capacitor plates giving rise to free charge separated by a distance, thus creating a voltage source.
A major difficulty with this particular capacitive source is that as soon as it begins to provide current, the capacitor
discharges, and the output voltage decreases to negligible values. Another difficulty arises from the fact that the
piezoelectric material/capacitive structure provides charge that is largely fixed by the piezoelectric coupling
coefficient, while at the same time, PZT has a very large dielectric constant. Therefore, while a relatively large
capacitance may result, since the charge is fixed, a relatively small voltage may be induced.
The challenge will be to develop electronics which can sense this small charge and/or small voltage. Another important
issue is that since the voltage source is capacitive in nature, only voltage amplifiers which draw virtually no current
will be appropriate for sensing. Thus, amplifiers will need to be MOSFET based. However, at the same time we have
to be careful with respect to noise. Since the signals we are sensing can be relatively small, we must be careful not to
add additional noise from the circuit. This is especially important over the bandwidth at which the ARL’s PZT sensors
will operate, since at these frequencies, MOS electronics can be sensitive to 1/f (often called flicker) noise. To
minimize our noise, while at the same time sense small signals, one needs to design circuits which maximize
transconductance, but at the same time minimize input capacitance. However, this will be a challenge since
transconductance is proportional to capacitance. We will therefore develop an optimization scheme where an optimal
balance between low noise generation and high gain (requiring large input capacitance), and low source loading
(requiring low input capacitive) is achieved. To realize this we plan to develop a multi-stage topology with
approximately 20dB gain in stage one. Additionally, the amplifier will be bandwidth limited to cover only the
operational frequencies of the PZT sensor, and thus reject noise from outside the band of interest. Optimization
programs will be developed using the CAD tool MATLAB. Circuits will be designed with the aid of the CAD program
Cadence, and submitted to the MOSIS foundry service for fabrication and tested both at UMCP and ARL laboratories.
IIIb. Analog to Digital Conversion with Range Compression for MEMS: The signals coming from the MEMS devices
will be analog. However, it is often convenient to process theses signal digitally. We therefore plan to develop analogto-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 2:
Figure 2. “Companding” flash ADC. The resistor “weights” are
set to create any desired compression function.
IIIc. 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.
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.
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.
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.
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.
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