Chip and System Design for On-Body Wireless Sensing

Prof. Brian OtisUniversity of WashingtonElectrical EngineeringSeattle, WA

Prof. Jeremy HollemanUniversity of TennesseeElectrical Engineering & Computer ScienceKnoxville, TN

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Focus of this talk: system building for wireless sensors

COTS vs. custom chips?

Wireless power vs. battery power?

Theoretical & practical design constraints

A few design examples

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RF Matching Network

Antenna connector

32kHz Watch Crystal

MCUMSP430F5528

FRAM1Mb

15 mm

RF Crystal

RF Digital TransceiverCC1101

Traditional miniaturized system design

External interface: power, SBW, SPI, I2C, GPIO, ADC

U.W. Encounternet

• Sub-gram digital peer-to-peer radios

• On-board storage of “encounter logs”, periodically upload to basestations

• Hundreds deployed in the field

• >10 years and millions of encounters logged

• >1k on order from researchers

Female Manakin, Costa Rica, 2011, Mennill et al.

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Typical power profile

t

Power

Tx (-10dBm)20mWRx

30mW

Standby<10μW

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Problems

Power too high for continual streaming applications (dominated by radio)

Size too large for emerging biomedical sensing paradigms

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Wireless communication modalities

1. Miniaturized battery-powered systems

2. Miniaturized wirelessly-powered systems

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Wireless communication modalities

1. Miniaturized battery-powered systems

2. Miniaturized wirelessly-powered systems

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Chip architecture

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350 mg

4 channel

18 ksps A/D

200 kbps Tx

22 hour

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Human ECG experiment

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Human ECG

Two hours of ECG data (>20Mb) streamed to a laptop and reconstructed

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Wireless communication modalities

1. Miniaturized battery powered systems

2. Miniaturized wirelessly-powered systems

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Case study: an RFID-like system

UHF Power Transfer FCC Limit Transmitter (+30dBm) 3m range ~30μW RF power available after modulation

losses

RFID provides nearly free uplink data transmission Tag modulates reflection coefficient

RF Power signal can also serve as frequency reference

Tag

Commercial UHF RFID Reader

BiosignalInput

Reader

Battery-Free Sensor Tag

Power

Data

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Architecture (Yeager et al., JSSC 2010)

ChopperAmp 8b ADC

3MHz Clock

UID

Mod

RectifierDemod

Bandgap 50nA Bias

1.8V Reg. 1.2V Reg. 0.7V Reg.

Diode Ref.

RX

TX

Gen2Tag

Logic

RNG

Sensor

OptionalmC, amps

Good: leverage Gen2 RFID protocol & infrastructure

Bad: Gen2 RFID protocol overhead

Chopper

RF FE

DigCore

UID

TestStructures

2.5mm

ADC

PCB Front PCB Back

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Battery-free wireless sensor

-12 dBm sensitivity 3m range

RFID Gen2 Compatible Talks to COTS Readers

Zero trimming On-chip biosignal

amps, ADC, etc

Hawkmoth Experiment

Prof. Tom Daniel, U.W. Biology

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Power source comparison

1. Miniaturized battery powered systems

Much longer wireless range Relaxed antenna specifications More signal processing capability Potential for data logging

2. Miniaturized wirelessly-powered systems

No maintenance or battery changes No need for stable clock generation Ultimate minimization of form-factor & mass

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Wireless Neural Interfaces Potential to revolutionize neuroscience research; medicine: epilepsy, Parkinson’s, hearing loss, SCI. Noise, power, long-term reliability serious obstacles

Modality Bandwidth

Amplitude

Spatial Resolution

Invasiveness

Single-Unit .1-7 kHz < 500 μV 0.2 mm Invasive

Cuff < 5 kHz < 10 μV - Mod. Inv.

LFP < 200 Hz < 5 mV 1 mm Mod. Inv.

ECoG .5-200 Hz < 100 μV 5 mm Mod. Inv.

EEG < 100 Hz 10-20 μV 30 mm Non-Inv.

EMG < 1 kHz < 10 mVInterferers

Power Line 50/60 Hz 10’s mV

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Are we there yet?

Adequate noise/BW for EEG, ECoG, single-unit Not sufficient for cuff electrode recordings

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Signals in the Noise?

Noise specifications often based on “background noise” Aggregate neural activity of

many distant units Signal processing can

potentially pull more information from a recording with sufficiently low noise

Single-unit signal quality deteriorates dramatically over time Tissue reaction covers

electrode

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Noise: How good is good enough?

Fundamental limit: Electrode resistance generates noise NB: Impedance ≠

Resistance Amp Noise < .46

Electrode noise for < 10% SNR degradation

R (kΩ)

en (nV/√H

z)

Area (mm2

)Rubehn 2009 1.7 5.3 3

Takahashi 2007 134 47 .006

Molina-Luna 2007

7.5 11 .01

Existing amps leave information on the table

Want to be here

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How do we get there - More Power?

Power & noise typically trade off Tissue necrosis a risk at P > 80 mW/cm2

Low-frequency noise (1/f) severe in neural frequencies; Scales w/ area, not power

Current amps approaching practical constraints on transistor area

For low-channel-count systems, increased power & area offers modest improvement potential

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How - Improved Amp Topologies? Single-ended amplifier,

complementary input Current ➘ by 4x Poor power supply rejection

Comp. differential amp Improved linearity, PSRR

Feed-forward Error correction Measure VDD noise,

& compensate

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So what?

1. We can make extremely small battery-powered and battery-free wireless systems

2. Wireless connectivity is typically the bottleneck in on-body sensing

3. Significant circuit design problems remaining

4. Chip designers can have a profound influence on next generation biomedical sensing systems

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Questions?

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Access point

USB/PC interface Analog out for audio spike observation LEDs indicate signal strength Indoor range ~24 meters

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Data (and metadata)

Mouse barrel cortex recording(collaboration with the Allen Institute for Brain Sciences)

Session statistics

Long term in vivo recording performed overnight

11.5 hours of data collected

Over 750 million samples received wirelessly(> 6 Gb of data)

1.4*10-7 % data loss due to dropped packets

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How do we get there – Devices?

BJTs Base current, input

resistance can (maybe) be cancelled

Relecin,b> 2ICq/β

JFETs

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Technology Impact CostReliabilitySecurityUsabilityStandards

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How - Chopper modulation?

Moves signal to higher frequency to avoid 1/f noise

Modulation converts Cin to resistance Rin = fChop×CIn

1/f noise power ∝ 1/Cin

Noise reduction requires ➚fChop or ➚Cin

Either way, roughly constant noise/Rin

Unless Rin >> Relec CM-DM conversion worsens EMG interference

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Let’s build a system