Recent Advances in Neural Dust, a platform for peripheral and recording Michel M. Maharbiz DJ Seo, Ryan Neely, Konlin Shen Jose Carmena, Elad Alon, Jan Rabaey

Electrical Engineering and Computer Science University of California, Berkeley

2016 NSF Nanoscale Science and Engineering Grantees Conference 12 December 2016 the Neural Dust concept

• Idea conceived in March 2013 • Technical rationale published in arXiv in July 2013

http://arxiv.org/abs/1307.2196

• First in vitro demonstration, J Neurosci Meth, Nov 2014

• 1st gen beam-steering system, IEEE EMBC 2015, August 2015

• In vivo PNS data, Neuron, August 2016 How do we get to chronic interfaces?

[Doerner 2010] [Hochberg Nature 2006] [Muller JSSC 2015]

Today’s systems Bulky, invasive, wired, low-density

Future systems Low-power, scalable, wireless systems  1.25 mm2, 2.5 µW power consumption [Biederman JSSC 2013]

[Biederman JSSC 2013] [Yin Neuron 2014] Extreme Miniaturization Important for recording resolution and longevity of sensors Resolution – observation at the cellular level • Neurons are 10 – 100 µm in diameter • Need spatial measurement resolution [Courtesy of T. Blanche] on the scale of 10’s µm

Reliability and Longevity

• Recorded signal quality reduced by scar tissue • Small fraction of patient’s lifetime • Not sufficient for chronic BMI [Turner 1999] • Miniaturized probe design can reduce scar tissue

[Potter 2012] [Seymour 2006] (slide courtesy of DJ Seo) Could we make a vanishing small RF interface to the nervous system?

RX

TX Drive

Two fundamental issues:

• A small form factor (volume) + speed of light  fres = 10’s GHz • Significant tissue loss at such high frequency

• Output power limit due to safety regulations: 10 mW/cm2 • e.g. 1 mm2 interrogator, 100 μm dust node, 2 mm distance  received power < 40 pW << 2.5 μW for CMOS theory Seo et al., arXiv 2013 Neural Dust system concept

• the interrogator couples ultrasound energy to the motes • the interrogator can perform both/either spatial and frequency discrimination with sufficient bandwidth/resolution to interrogate each mote • each mote consists of a piezoelectric transducer, surface electrodes for electrophysiological signal acquisition, and a silicon CMOS die containing electronics for signal amplification/conversion. • The mote reports recorded signals back to the interrogator by reflecting and modulating the amplitude, frequency, and/or phase of the impinging ultrasound wave. Neural Dust (ND) System Miniaturized ultrasonic platform can be used for CNS & PNS Sub-Dural Link Model

• TX (interrogator) and RX (neural dust) modeled with KLM • Match resonant frequency to maximize power transfer • 2 mm tissue as a lossy transmission line US Wireless Powering: Ultrasonic power transfer is much more efficient

Power Safety Limit Tissue Attenuation

7.2 mW/mm2 2.5 dB/mm

1 dB/mm

0.1 mW/mm2

US EM US EM 10 MHz 10 GHz

• Low acoustic velocity allows operation at a much lower frequency – e.g. λ = 150 μm @ 10 MHz US vs. λ = 5 mm @ 10 GHz EM

• The acoustic loss is smaller than EM loss – Safety regulation (0.1 mW/mm2 for EM vs. 7.2 mW/mm2 for US) Significantly more power with US

• Efficiency of ~7% (or -11.6 dB) at 100 μm • Received power: ~500 μW US vs ~40 pW EM (1 mm2 interrogator)

• Scaling prediction of 3.5 μW at 20 μm node Scaling: Electrode Modeling

Randles Model for Electrode

d [Du PLoS 2011] • Electrode has thermal noise 2 2 • Electrode |Z| density: Cdl ~ 0.5 pF/μm , Rs = 18.65 MΩ·μm • Voltages are measured differentially • Neural dust: reference electrode on the same footprint • e.g., measured signal amplitude for d = 100 μm is ~10 μV [Du 2011] Scaling of the mote

• Captured power decreases with mote size • Extracellular recording is differential, so signal decreases with size • smaller motes need more power to maintain same SNR • Fundamental electrode thermal noise

Scaling with an SNR of 10 dB shows operation down to 50 μm

Can exceed FDA safety regulation, but scaling is ultimately limited by electrode thermal noise How do you build the front end?

Simplified neural front-end with a single FET sensor

• Electrical load impedance (FET) varies with vneural • Instantaneous ultrasonic wave reflectivity changes • Backscattered wave is modified First experimental data Seo et al., J Neurosc Meth, 2014 (a) (b)

Initial validation of power coupling

• Measured power transfer efficiency at various mote sizes matches simulated behavior closely.

• For each mote dimension: • impedance spectroscopy • frequency response of harvested power on the PZT reinforces the reliability of the simulation framework. Initial validation of power coupling

Simulated backscatter sensitivity scaling plot for various impedance levels. in vivo rodent PNS data Seo et al., Neuron, in press dust motes and transceiver

[Seo EMBC 2015] [Tang ISSCC 2015] [Seo, Tang TBioCAS 2015]

Noise Floor & Tissue Attenuation System is currently sensitive to misalignment & misorientation

• Severe penalty with misalignment & misorientation • Effect of lateral misalignment follows the beam pattern power fall-off • Angular misalignment is less severe than lateral misalignment • Can be addressed by spatial multiplexing and/or more robust reconstruction algorithms (Future Directions) EMG preparation • transducer is placed ~1 cm away from mote • 6 pulses, 1.8 MHz every 100 µs • 720 µW total output average power • 8 mW/cm2 average ISPTA (FDA -> 720 mW/cm2) • 0.3 mW/cm2 peak ISPTA (FDA -> 190 W/cm2) 100 µs • the transceiver consumes 500 µW but heavily duty-cycled (0.008%) so 1.8 V operation is possible Graded EMG data a) voltage recorded at implanted “ground truth” electrodes vs. electrophysiological electrode stim intensity b) voltage reported by dust mote vs. electrophysiological electrode stim intensity c) one-to-one comparison of “ground truth” vs. dust mote electrodes at 100% stimulation intensity d) voltage error between signals in c) e) neural dust EMG peak-to-peak voltage vs. stim intensity showing classic sigmoidal curve

100 kHz sample rate for “ground truth” 10 kHz sample rate for dust motes ENG preparation in the sciatic

Adapted from Advanced microsurgery, 2. In vivo laboratory surgery, The University of Szeged. ENG data b) compound potential recorded at implanted “ground truth” electrodes vs. electro-physiological electrode stim intensity c) voltage reported by dust mote vs. electrophysiological electrode stim intensity d) one-to-one comparison of “ground truth” vs. dust mote electrodes at 76% stimulation intensity e) voltage error between signals in c) f) neural dust ENG peak-to-peak voltage vs. stim intensity showing classic sigmoidal curve

200 kHz sample rate for “ground truth” 10 kHz sample rate for dust motes the future Beam-forming for aligning motes, targeting multiple motes and steering Seo et al., EMBC 2015

A custom ASIC drives a 7 x 2 PZT transducer array The measured acoustic-to-electrical conversion efficiency of the with 3 cycles of 32V square wave with a specific receive mote in water is 0.12% and the overall system delivers programmable time delay to focus the beam at the 26.3% of the power from the 1.8V power supply to the transducer 800 um neural dust mote placed 50mm away. drive output, consumes 0.75uJ per each transmit phase. Digital front end Amplifying the input signal can fully utilize the dynamic range

1

0.9

0.8

0.7

0.6

Backscatter (norm) 0.5

0.4 -600 -400 -200 0 200 400 600 Input Voltage (mV)

• Noise floor of the system limits detecting CNS signals

• Neural signal occupies a small region in the calibration curve • Potential usable dynamic range is wasted • Neural front-end, with appropriate amplifying chain, can utilize this by amplifying the input signal Digital Comm. ASIC Neural front-end and miller modulator are included

Full-wave AC-DC Rectifier Reference, Piezo + Regulator Cap Doubler

US- IN- 400 Mixed signal feedback LDO ref Dig

Rectifier µm 10b ADC LNA Modulator US+ IN+ (FET DAC

Bias Amp switch) Mod Offset cancellation ASIC 700 µm [Design largely adopted from Biederman [w/ Kyoungtae Lee] JSSC 2013 & Muller JSSC 2012]

Low-power neural front-end with digital backscatter • Powered by ultrasound and communication with miller modulation • 400 µm x 700 µm chip taped out in 65nm TSMC LP in November • Back in January for recording CNS in animal what’s coming • ND recording noise floor to < 10 µV • <500 micron dust • ND stimulation • (w/ Prof. Rikky Muller, Ben Johnson) what’s coming • Backpack for awake behaving neuromodulation • Biocompatible materials

• CNS! 3-5 years

• integrating new circuits and sensors • pressure • temperature • strain

• O2 • pH • …

• instrument tissue and organs, not just • fundamental platform for data in and out • Three examples: • GI tract microflora species tracking • chronic deep oxygen measurement • chronic tissue pressure/stiffness • fracture healing

• demonstrate chronic viability

Häggström, Mikael. "Medical gallery of Mikael Häggström 2014". Wikiversity Journal of Medicine 1 (2). DOI:10.15347/wjm/2014.008. ISSN 20018762 recent grad students Questions? Arda Ozilgen David Piech faculty collaborators Thanks! Brian Kilberg Elad Alon Amy Liao Jose Carmena DJ Seo Jan Rabaey Konlin Shen Caroline Ajo-Franklin Monica Lin Kristofer Pister Tom Zadjel Hirotaka Sato Travis Massey Bochao Lu Murat Arcak Stephanie Garcia Adam Arkin Camilo Diaz-Botia Flip Sabes Alyssa Zhou Peter Ledochowitsch Brian Pepin undergraduates Daniel Cohen Robin Herbert Michael Lorek Kaylee Mann Gabriel Lavella Jonathan Chen Svetoslav Kolev Vedavalli Krishnan Nimbus Goehausen Myo Myo Nyi Ryan Tseng Yoav Peeri Emin Baghoomian Chris Berry … Piezoelectric XDCR

• XDCR model using 3-port network, based on KLM model (1970) • Both electrical and mechanical resonances • Determined by the thickness of the XDCR • Aspect ratio: Interrogator (10:10:1), neural dust (1:1:1) for density

Beam spreading and mote position Interrogator

Tissue Depth (d = 2 mm)

푫ퟐ Depth (mm) Rayleigh Distance = ퟒ흀 • 3D loss mechanism: beam spreading • Dust mote should be placed at interrogator’s Rayleigh distance • Interrogator sized (1 mm2) to match its Rayleigh distance (natural focus) with tissue transmission distance (d = 2 mm) @ 10 MHz • Beam steering to enable multi-node interrogation (more later) BaTiO Cube Thickness Resonance mode (z) in x BaTiO Cube Thickness Resonance mode (z) in y 3 3 -12 -12 10 10

X: 1.5e+07 X: 1.5e+07 -14 Y: 5.51e-15 -14 Y: 5.5e-15 10 X: 2.26e+07 10 X: 2.26e+07 Y: 3.966e-14 Y: 3.813e-14

-16 -16 10 10

-18 -18 10 Cube:10 Mode Coupling (Re-Radiation) -20 -20 10 10 1 1.5 2 2.5 3 3.5 4 1 1.5 2 2.5 3 3.5 4 7 7 x 10 x 10

BaTiO Cube Thickness Resonance mode (z) in z BaTiO Cube Thickness Resonance mode (z) 3 3 -12 -12 10 10 Simulation Result: Energy in x • @15MHz (1st resonance) Energy in y -14 X: 2.26e+07 -14 10 10 Ex/Etotal = 16.6% X: 1.5e+07 Y: 1.109e-13 Energy in z Y: 2.218e-14 Ey/Etotal = 16.6%

-16 -16 Ez/Etotal = 66.8% 10 10 • @22.6MHz (2nd resonance) -18 -18 10 10 Ex/Etotal = 21.0% Ey/Etotal = 20.2% -20 -20 10 10 Ez/Etotal = 58.8% 1 1.5 2 2.5 3 3.5 4 1 1.5 2 2.5 3 3.5 4 7 7 x 10 freq (Hz) x 10

• Re-radiation along two perpendicular axes due to Poisson’s ratio • For perfect cube, ~66% of the energy kept in the main thickness resonance mode • Modeled as additional loss