Engineering systems in a cubic millimeter and beyond
see link below for all images
Kris Pister
Berkeley Sensor & Actuator Center
Electrical Engineering & Computer Sciences
UC Berkeley – pister@eecs.berkeley.edu
(on leave to start Dust Inc – kpister@dust-inc.com)
Outline
• Brief History
• “COTS” Dust
• Optical Communication
• Miniaturization
• Mobility
Interdisciplinary Research
• Engineering: Electrical, Mechanical, Chemical, Aerospace
• Computer Sciences
• Chemistry, Physics
• Biology and Bioengineering
• Nanotechnology
Ken Wise, U. Michigan
http://www.eecs.umich.edu/~wise/Research/Overview/wise_research.pdf
Bill Kaiser, UCLA
• http://www.janet.ucla.edu/WINS
Wireless dawn sensor
Computation
Difference Engine
Charles Babbage, 1822
Steve Smith, UCB
Multi-hop message passing
Lots of exponentials
• Digital circuits
• Speed, memory
• Size, power, cost
• Communication circuits
• Range, data rate
• Size, power, cost
• MEMS Sensors
• Measurands, sensitivity
• Size, power, cost
Computation Communication
Sensing
Smart Dust Goal
COTS Dust - RF Motes
• Simple computer
• Cordless phone radio
• Up to 2 year battery life
N
S
W E 2 Axis Magnetic
Sensor
2 Axis
Accelerometer
Light Intensity
Sensor
Humidity Sensor
Pressure Sensor
Temperature Sensor
Open Experimental Platform to
Catalyze a Community
Small microcontroller
- 8 kb code, 512 B data
Simple, low-power radio
- 10 kb
EEPROM storage (32 KB)
Simple sensors
WeC 99
“Smart Rock” Mica 02
NEST open exp. platform
128 KB code, 4 KB data
50 KB radio
512 KB Flash
comm accelerators
Dot 01
Demonstrate
scale
Rene 00
Designed for
experimentation
-sensor boards
-power boards
TinyOS
Networking
Services
David Culler, UCB
TinyOS/COTS-Dust Platform Users
• INTEL CORPORATION
• INTEL RESEARCH
• JPL
• KENT STATE UNIVERSITY
• LAWRENCE BERKELEY NAT'L
• LLNL
• LOS ALAMOS NATIONAL LAB
• MARYLAND PROCUREMENT
• MIT
• MITRE CORP.
• MSE TECH. APPLICATION INC
• NASA LANGLEY RESEARCH CTR
• NAT'L INST OF STD & TECH
• NICK OLIVAS LOS ALAMOS NA
• NORTH DAKOTA STATE UNIV
• PENNSYLVANIA STATE UNIV
• ROBERT BOSCH CORP.
• RUIZ-SANDOVAL, M.E.
• RUTGERS STATE UNIVERSITY
• SANDIA NATIONAL LABS
• SIEMENS BUILDING TECH INC
• SILICON SENSING SYSTEMS
• SOUTHWEST RESEARCH
• TEMPLE UNIVERSITY
• ACCENTURE
• ALLEN, ANTHONY
• ALTARUM
• BAE SYSTEMS CONTROLS
• BALBOA INSTRUMENTS
• BOSCH
• BUDNICK, LARRY
• CARNEGIE MELLON UNIV
• CENTRID
• CLEVELAND STATE UNIV
• CORNELL UNIVERSITY
• DARTMOUTH COLLEGE
• DOBLE ENGINEERING
COMPANY
• DUKE UNIVERSITY
• FRANCE TELECOM R&D
• GE KAYE INSTRUMENTS, INC
• GEORGE WASHINGTON UNIV.
• GEORGIA TECH RESEARCH INT
• GE
• GRAVITON, INC
• HRL ABORATORIES
• UNIV SOUTHERN CALIFORNIA
• UNIVERSITY OF CALIFORNIA
• UNIVERSITY OF CINCINNATI
• UNIVERSITY OF COLORADO
• UNIVERSITY OF ILLINOIS
• UNIVERSITY OF IOWA
• UNIVERSITY OF KANSAS
• UNIVERSITY OF MICHIGAN
• UNIVERSITY OF NOTRE DAME
• UNIVERSITY OF SOUTHERN CA
• UNIVERSITY OF TEXAS
• UNIVERSITY OF UTAH
• UNIVERSITY OF VIRGINIA
• US ARMY CECOM
• USC INFORMATION SCIENCES
• VANDERBILT UNIVERSITY
• VIGILANZ SYSTEMS
• VITRONICS INC
• WASHINGTON UNIVERSITY
• WAYNE STATE UNIVERSITY
• WILLOW TECHNOLOGIES LTD
• WJM, INC
• XEROX
30 Universities
8 Govt./National Labs
68 Total
800 node demo at Intel Developers Forum
4 sensors
$70,000 / 1000
Concept to demo in 30 days!
Structural performance due to multi-directional
ground motions (Glaser & CalTech)
Comparison of Results
Cory Energy Monitoring/Mgmt
System
• 50 nodes on 4th floor
• 5 level ad hoc net
• 30 sec sampling
• 250K samples to database over 6 weeks
29 Palms Sensorweb Experiment
• Goals
• Deploy a sensor network onto a road from an unmanned
aerial vehicle (UAV)
• Detect and track vehicles passing through the network
• Transfer vehicle track information from the ground network
to the UAV
• Transfer vehicle track information from the UAV to an
observer at the base camp.
Flight Data
Dragon Wagon
From UAV
Dragon Wagon
HMMWV
From UAV
HMMWV
Last 2 of 6 motes are dropped
from UAV
• 8 packaged motes loaded
on plane
Last 2 of six being dropped
Detection algorithm
Ax b
t
t
t
t
t
v
p
p
p
p
→ =
= 4321 4321 1/ 1111 δ
Each vehicle V(v,δt) has two parameters:
1) Speed (v)
2) Time at beginning of network (δt)
The n-node network is described by an n-entry pattern vector p:
The jth entry is the time we expect that node j will see V(1,0)
Times when nodes detect V are collected in the t vector
Linear least-squares guess at v and δt
Room to spare!
Volcano UAV
• Fully autonomous
• 14’ wingspan
• 30-90 mph, 50 mph cruise
• 25 lb airframe, 75+ takeoff weight
• 5000 km range, no payload
• 1000 km = 100 motes (today)
Bald Camel
• 50 motes
• Microphone
• Magnetometer
• Micro-impulse radar
• 2 satellite phones
• Deployed from a Predator
Available Sensors
• Demonstrated w/ COTS Dust
• Temperature, light, humidity, pressure, air flow
• Acceleration, vibration, tilt, rotation
• Sound
• GPS
• Gases (CO, CO2)
• Passive Infra-red
• Contact/touch
• Available
• Images, low-res video
• Gases (VOCs, Organophosphates, NOx…)
• Neutrons
Demonstrated Actuators
• Motor controllers
• 110 VAC relays
• Audio speaker
• RS232: LCD, …
Optical Communication
• SALT – steered agile laser transceiver
• Dust
Video Semaphore Decoding
Diverged beam @ 5.2 km
In shadow in evening sun
~8mm3 laser scanner
Two 4-bit mechanical DACs
control mirror scan angles.
~6 degrees azimuth, 3 elevation
1Mbps
CMOS Imaging Detector
Photosensor
Signal Processing
A/D Conversion
SIPO Shift
Register
CRC Check
Local Bus Driver
Off Chip
Bus Driver
Pixel Array
Goal 1:1 Mbps, 5 km, 1 cm3
Goal 2: 1Mbps, 5km between UAVs
Optical Limits: Examples
Pixel
Array
Receiver
FOV
Receiver
Aperture
Transmitter
HWHM
Link
Bit Rate Range
100 Mbps 10 m 5 ° 10 mm 90 ° 8 × 8
1 Mbps 500 km 0.2 mrad 20 cm 5 ° 16 × 16
1 Mbps 5 km 1 mrad 10 mm 10 ° 8 × 8
Ptrans = 5 mW (opt.) Optical SNR > 20 dB Recv’d Power > -54 dBm
λ = 830 nm ∆λ = 10 nm Rphoto = 0.25 A/W
MAV/MAV
Sensor/LEO
office
Looking East from Berkeley Marina
~1 steradian
+/- 10 degrees
Cory Evans Campanile
5.1 km link test
Data from 5.1 km link
• 15mW, 1mrad, 1Mbps TX
• 2cm RX aperture
• 40nW optical power received
• Low link availability due to turbulence
Smart Dust Electronics
13 state
FSM
controller
ADC
100kS/s
2uW
ambient light
sensor
Photodiode
Sensor input
Oscillator
Power input Power
TX Drivers
0-100kbps
CCR or diode
Optical Receiver
1 Mbps, 11uW
1mm
330µm
Isolation trenches are etched through
an SOI wafer and backfilled with nitride
and undoped polysilicon.
Smart Dust MEMS
Power, sensor, motor fab
Solar cells and circuits are created
by ion implantation, drive-in, oxidation,
contact etching, aluminum sputtering
and etching.
Actuators are deep reactive ion etched
through device layer.
Power, sensor, motor fab
Optional backside etch (would actually precede front side etch)
Power, sensor, motor fab
Solar Cell Results
Solar Array Performance
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0 5 10 15 20 25 30
Voltage (V)
Current (uA)
0.5 to 100 V demonstrated
10-14% efficiency
Closing in on 1mm3
2.8mm
2.1mmCCR Accelerometer
Solar Cells
CMOS IC
Smart Dust - Integration
Solar Cell Array CCR
XL
CMOS
IC
16 mm3 total circumscribed volume
~4.8 mm3 total displaced volume
SENSORS ADC FSM RECEIVER
TRANSMITTER
SOLAR POWER
1V 1V 1V 2V 3-8V
PHOTO 8-bits
375 kbps
175 bps
1-2V
OPTICAL IN
OPTICAL OUT
Mote with Micro-battery from Lee & Lin, UCB
~2 mm^2 ASIC
RF mote
• CMOS ASIC
• 8 bit microcontroller
• Custom interface circuits
• External components
uP SRAM
ADC Radio
Temp
Amp inductor
crystal
battery
antenna
~$1
First Prototype Layout
• IO Pads
• RAM blocks
• MMU logic
• Debug logic
• ADC
• CPU Core
• RF Place Holder
2mm
Fabbed, functional
Second Prototype
8 bit uP, 3kB RAM
8 bit ADC
Data encryption
900 MHz RF TX
Talking to Blue mote
at 19.2kbps, 10m
Working mote, happy grad student
Jason Hill
Jason’s mote
Power and Energy
• Sources
• Solar cells ~0.1mW/mm2, ~1J/day/mm2
• Combustion/Thermopiles
• Storage
• Batteries ~1 J/mm3
• Capacitors ~0.01 J/mm3
• Usage
• Digital computation: nJ/instruction
• Analog circuitry: nJ/sample
• Communication: nJ/bit
10 pJ
20 pJ/sample
11 pJ RX, 2pJ TX (optical)
10 nJ/bit RF
Energy and Lifetime
• 1 mAh ~= 1 micro*Amp*month (µAm)
• Lithium coin cell: 220 µAm (CR2032, $0.16)
• AA alkaline ~ 2000 µAm
• 100kS/s sensor acquisition: 2µA
• 1 MIPS custom processor: 10µA
• 100 kbps, 10-50 m radio: 300µA
• 1 month to 1 year at 100% duty
• 10 year lifetime w/ coin cell 1% duty
• Sample, think, listen, talk, forward…
2 times/second!
Mike Sailor’s Smart Dust
M. Sailor
UCSD Chemistry
Integrated Microwatt Transceiver,
Howe/Rabaey, UCB
fclock
Rejects non-linear LNA components
Shapes LNA thermal noise
Selects System Frequency Bands
Prefilter: micromachined LC passive
RF Filter
(Low Q)
A
D
LNA
NM Filter
NM Filter
NM Filter
•Radios need filters
•The best filters are electromechanical
•Power is related to size
Nano Dust?
• Nanotube sensors
• Nanotube computation
• Nanotube hydrogen storage
• Nanomechanical filters for communication!
Mobility
• Walking
• Hopping
• Flying
Mobility
Power from MEMS Combustion Power from MEMS Combustion
Thermopiles
Nozzle
(w/ igniter)
Milli-Millennium Falcon
Increase the thrust and Increase the thrust and
decrease the mass, decrease the mass,
while controlling while controlling
thermal losses thermal losses
Thrust Measurements vs. Theory
0
5
10
15
20
25
0 0.5 1 1.5 2 2.5
Time (sec)
Thrust (millinewtons)
Predicted altitude: 50 m
Rocket in Action
Synthetic Insects
(Smart Dust with Legs)
Goal: Make silicon walk.
•Autonomous
•Articulated
•Size ~ 1-10 mm
•Speed ~ 1mm/s
2 Degree of Freedom Legs
1st Link
Motor
2nd Link
Motor
1mm
Silicon Inchworm Motors
1mm
Current Layout for Motor and Legs
Legs
7.6 mm
Solar CellsMotor
Motor
CMOS Linkages
Solar Powered Robot Pushups
Big Products from Small Workers
The Dark Side
Conclusion
• Tremendous promise
• More new questions than answers
