Miniaturized Wireless Sensing for Process Monitoring

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Prof. Brian Otis, Prof. Babak Parviz University of Washington Electrical Engineering Department Seattle, WA, USA Miniaturized Wireless Sensing for Process Monitoring

Transcript of Miniaturized Wireless Sensing for Process Monitoring

Page 1: Miniaturized Wireless Sensing for Process Monitoring

Prof. Brian Otis,  Prof. Babak ParvizUniversity of Washington 

Electrical Engineering DepartmentSeattle, WA, USA

Miniaturized Wireless Sensing for Process Monitoring

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Outline

• Motivation: inexpensive, microscale wireless process monitors

• Technical challenges

• New results: 500mg wireless temperature sensor

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Goal:  improved process observabilityusing distributed sensors

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From the large scale…

• Example: Structural monitors, fluid conductivity, flow rate, temperature, etc, at arbitrary locations 

• Unrealistic to position thousands of complete sensing and chemical analysis units

• Use large number of inexpensive wireless monitors

• Network density provides high spatial resolution and robustness

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…to the micro scale

• Dense networks of interconnected wireless sensors• Allow fine‐grained measurement of process parameters• Sub‐mm device sizes

* Prof. Albert Folch, University of Washington

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Challenges: cost, size, power

• Necessary to integrate many different technologies in a very small volume:

– Various sensors

– RF transceiver/antenna

– Microprocessor

– Battery/Power supply

– Passive components

• The resulting system:

– Too large for microscale sensing

– Prohibitively expensive for ubiquitous deployment

– Power is a huge problem

NI Data Acquisition Card 

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Moore’s Law to the rescue..

The number of transistorson an IC doubles every 2 years

Resulting from advancements in IC fabrication and design automation

1960: 25um MOS process

Today: 0.032um MOS process

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1. Computation and signal processing can now be performed in a few mm2

2. This computational power is growing exponentially

3. Scientists in many communities (wildlife tracking, process monitoring, neural engineering) are recognizing this and are eager to take advantage of this opportunity

4. Can enable previously impossible collection of data

Why is this important?

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We can shrink the electronics…

But what about power?

Battery technology can’t keep pace with Moore’s law

Towards energy harvesting for autonomous sensors...

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Good candidates for process monitors:   RF, solar, thermal, vibrational.

Energy harvesting

Extract energy from the environment to power wireless sensors

S. Roundy, B. Otis, Y.H. Chee, J. Rabaey, P. Wright, IEEE ISLPED 2003

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Thermal power

1cm

Battery replacement:

‐ 1cm3 footprint‐ 10uW average power‐ Provide heat flow sensing?

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Wireless power transfer

• Asymmetric link: shifts burden of power and complexity to the interrogator

• Allows very small, inexpensive sensing tags

sensor

Detect

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Case study: a wireless temperature sensor

• Goal: miniaturized, battery‐free, wireless sensor for monitoring temperature levels in chemical processes, pipelines, and storage tanks

• Also applicable to automotive, aviation, industrial, and medical monitoring

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Wireless sensor architecture

+RFID circuitry

RFID + sensor â€œtag”

RFID reader

Wireless power

Sensor ID, Data

1‐10m

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The power challenge

• Temperature sensing requires precision amplification and analog to digital conversion (ADC)

• Unstable power supply due to wireless power transfer

• Digital computation required for processing the RFID protocol

• The range and functionality of the system is limited by the amount of power that can be wirelessly transferred

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Prototype electronics

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Wireless sensor platform

• Power provided wirelessly from 900 MHz commercial RFID‐tag reader: 1m range

• Maintenance free

• Energy is stored on the board to power sensor‐interface

• Custom low noise IC

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Initial deployment: wireless neural interface

• Use commercial 900MHz RFID reader

• Communication through EPC Gen2 protocol 

• Single‐channel far field neural recording device

• 1m range

J. Holleman, D. Yeager, R. Prasad, J.R. Smith, and B. Otis, BioCAS, November 2008

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• Returns spike count instead of electronic product code (EPC)

• Can digitize and transmit short bursts of data after triggering Wireless charging Sensing

(RF Off)Backscatter data

Example: wireless EMG recording on a hawkmoth

Spike detector

Low noise amp

Initial deployment: wireless neural interface

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Current research: miniaturization

• Integration of RF, sensor interface, and computation onto a single integrated circuit

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SoC WISP

• Assembly completed 7/22/2009

• Single‐chip wirelessly powered sensor interface

• 0.13Îźm CMOS• ~ 100,000 transistors

• Dimensions: 1mm x 2mm x 0.15mm

• Designers: â€“ Dan Yeager

– Azin Zarrasvand

– Helen Zhang

1mm x 2mm<10ÎźW power dissipation

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SoC WISP

• 2ÎźW chopper stabilized amplifier• 8‐bit analog‐to‐digital converter

• RF energy harvester

• Three voltage regulators (0.7V, 1.2V, 1.8V)

• Digital RFID Gen2 protocol

• Unique chip identification generator

• Supply/process independent 8MHz oscillator

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RF Energy Harvesting Performance

• Sufficient operating voltage at an input RF power of â€16dBm (~25ÎźW)

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Wirelessly‐powered temp sensor

• Connection to generic thermocouple allows wireless, battery‐free temperature sensing

• ~ 500mg including antenna

• Range: ~2m with standard commercial RFID reader

• We’ve developed a GUI allowing programmable averaging and data logging

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Wirelessly‐powered temp sensor

• Connection to generic thermocouple allows wireless, battery‐free temperature sensing

• ~ 500mg including antenna

• Range: ~2m with standard commercial RFID reader

• We’ve developed a GUI allowing programmable averaging and data logging

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Wirelessly‐powered temp sensor

• Connection to generic thermocouple allows wireless, battery‐free temperature sensing

• ~ 500mg including antenna

• Range: ~2m with standard commercial RFID reader

• We’ve developed a GUI allowing programmable averaging and data logging

~40 reads/second

Adjustable averaging

Real-time readout

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Miniaturized flight recorder for hawkmoths 

Longitudinal section of Manduca sexta

DLM’s

• Initial deployment: recording temperature in‐flight from the Dorsolongitudinal (DLM) Muscles (main muscles that indirectly power the downstroke of the wings)

• Allows recording of previously impossible data

• Collaboration with Nicole George, Prof. Tom Daniel, UW Biology

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Conclusions

1. Moore’s law: exponential growth in computational power

2. There’s also a dramatic growth in performance of wireless sensing technologies

3. We have demonstrated early prototypes of a few key technologies

4. Recent results: battery‐free wireless temperature sensor integrated circuit