ASSIST Industry Meeting

Thursday January 26, 2017 – Raleigh, NC

1

Welcome

Dean Louis Martin-Vega, College of Engineering

2

ASSIST Overview

Dr. Veena Misra, ASSIST Center Director

3

4

Veena Misra, Director, Distinguished Professor of ECE, NCSUMehmet Ozturk, Deputy Director, Professor of ECE, NCSU

2017 Industry MeetingJanuary 26th–27th, 2017

44444444444444

Advanced Self-Powered Systems of Integrated Sensors and

Technologies (ASSIST)

Goals of this meeting

Update industry members on our systems driven researchResearch updatePoster/demoLab Tours

Recruit prospective members Networking Feedback on new models of industry engagementFeedback on sustainability

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What is ASSIST ?Competitively won in 2012 (NSF)

$40Million for up to 10 years

8 universities, >100 graduate students, >120 undergraduates and over 40 faculty members

~30 Industry members(~20 voting members)

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Research

Industry Ecosystem

Education and

Outreach

ASSIST’s vision is to use nanotechnology to impact healthcare and manage wellness

By building self-powered wearable, wireless, multiple sensor platforms that enable:

7

Long-term monitoring of personal health & environment

Pathway towards personalized medicine

Correlation of multiple sensors

Systems Driven Research (via Testbeds)

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2. Health and Environmental Tracker (HET)Clinically validated low power sensors for health and environment for

HET 1.0: Asthma (ozone, respiration and accelerometry)

HET 2.0: Diabetes (glucose and lactates from sweat)

Data correlationLow power operation to extended batteryWearability

1. Self Powered Adaptive Platform (SAP)Continuous/vigilant monitoring of critical health metrics

SAP 1.0: Cardiovascular disease (ECG, accelerometry)

Powered by the human bodyContinuous wearabilityIndefinite operational lifetime

Cardiovascular Disease

Asthma

Diabetes

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Critical Components to Achieve Testbed Functionality

Energy Harvesting and Storage

Low Power Electronics (SoCs and Radios)

Wearability & Data

Emerging Devices/Non-Volatile

Architectures

Low Power Sensors (health and env)

HET 1.0 AsthmaSAP 1.0 Cardiovascular HET 2.0 Diabetes

Disruptive Systems

Susan Trolier-McKinstry

Ben Calhoun

Suman Datta

Omer Oralkan

Jess Jur

Jason Strohmaier

John Lach

Alper Bozkurt

Meet our ASSIST PIs:

From Right to Left: Row 1: Drs. Veena Misra (NCSU), Philip Bradford (NCSU), Alper Bozkurt (NCSU), Shekhar Bhansali (FIU), Benton Calhoun (UVA), Michael Daniele (NCSU), Suman Datta (PSU), Michael Dickey (NCSU), Jesse Jur (NCSU), Mehdi Kiani (PSU)Row 2: Drs. John Lach (UVA), Edgar Lobaton (NCSU), Vijaykrishnan Narayanan (PSU), Omer Oralkan (NCSU), Mehmet Ozturk (NCSU), Nezih Pala (FIU), David Peden (UNC School of Medicine), Clive Randall (PSU), Chris Rahn (PSU)Row 3: Drs. Shad Roundy (UoUtah), Amy Snipes (PSU), Susan Troiler-McKinstry (PSU), Daryoosh Vashaee(NCSU), Orlin Velev (NCSU), Chunlei Wang (FIU), David Wentzloff (UMich), Douglas Werner (PSU) 11

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Multi-chip SoC: central SoC < 1 W

Energy Harvesting: Heat

Energy Storage

Ultra Low Power Electronics

Antenna with PDMS/AgNWsshows world record efficiency of 80%

Textiles/Wearability

Energy Harvesting: Motion

300k

Start_freq

Stop_freq

300k

-300k

-300k

50k

Measured freq_drift range based on a 1/1000 PER

BLE standard defined freq_drift range

Ultra Low Power Radios

Flexible Antenna

SAP 1.0 Cardiovascular

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Low Power Ozone and VOC

Data Activity Identification

Low power PPG

Data Algorithms

0 200 400

0.000

0.005

0.010

0.015dR/dtO3 Concentration

Nor

mal

ized

dR

/dt (

/s)

Time (s)

100 cycles Sn0.95Ti0.05O2 Film O3 Response

0

20

40

60

80

100

120

O3

Con

cent

ratio

n (p

pb)

HET 1.0Asthma

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Breathable Materials for Sweat Collection

Low Power PotentiostatData Validity

IRB Studies

Sensor Mechanisms

0 10 20 30 40

0.000.250.500.751.00

Vol

ume

(L)

Time (Min)

HET 2.0Diabetes

ASSIST Research PortfolioEnergy Harvesting and Storage

Emerging Low Power Nanoelectronics

Low power health and environmental sensors

Wearability and Data and Testbeds

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Body Heat Harvesting

Body Motion Harvesting

Electrochemical Supercapacitors

Ultimate Energy Efficiency Devices

Non-volatile architectures

Ultra low power SoC

Ultra Low power Radios

Body Worn Antenna

Ozone and VOC Sensing

Epidermal Biosensors ISFLow Power

Pulse-Oximetry

Data Human FactorsSmart Textiles

Power Management

ZnO based Sweat sensing

Wound Healing

Systems Research

Miniaturized Hybrid Capacitors

Biomimetic Sweat Sensing

Testbeds

ASSIST Research PortfolioEnergy Harvesting and Storage

Emerging Low Power Nanoelectronics

Low power health and environmental sensors

Wearability and Data and Testbeds

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Body Heat Harvesting

Body Motion Harvesting

Electrochemical Supercapacitors

Ultimate Energy Efficiency Devices

Non-volatile architectures

Ultra low power SoC

Ultra Low power Radios

Body Worn Antenna

Ozone and VOC Sensing

Epidermal Biosensors ISFLow Power

Pulse-Oximetry

Data Human FactorsSmart Textiles

Power Management

ZnO based Sweat sensing

Wound Healing

Systems Research

Miniaturized Hybrid Capacitors

Biomimetic Sweat Sensing

Testbeds

Body Heat Harvesting

Power Management

ppppppppUltra low power SoC

LLLLLLLLow Power Pulse-Oximetryy

BBBiomimetiiic Sweat Sensing

EEEpidermal lllBiosensors

Flexible Thermal Conductors

and TestbedsFFFFFFFlexible Thermall

Conductors

PresentationIndustry Collaboration

Student Pitch/Demos

Miniaturized Hyyyybrid Capacitorrs s s s s ss

Systems Research

Smart Textiles

OOOOOOOOOOOzone andndnddndndndndndnd VOVOVOVVVVV C Sensininininininnnggggggggg

Data

WWWWWWWWWoundddddddHeHeHeHeHeHeHeHH alinnggggggggg

BBBBBBody WornAntenna

BBBody Motioi nHaHarvestingngngngngngggg

Non-volatilearchitectures

ASSIST 10 Year Roadmap

17Y1

Peizoelectrics

Thermoelectrics

Supercapacitors

Gas/Particulate

Bioelectric

Data

Wearability

Low-power circuit

Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10

Biochemical

5 μ Watts

GENERATION 1

35-300 μ Watts

5-25 μ Watts

500 μ Watts 500 ~ 1000 μ Watts

>40 μ Watts

200 J/cc 600 J/cc 1000 J/cc

GENERATION 2 GENERATION 3, 4

O3 And VOC monitoring

EKG, EEG, and pulse oxymetry

Hydration, Cortisol monitoring Sweat, Glucose, Cortisol monitoring ISF

Blood pressure

VOC, NO2, O3, And CO monitoring Breath and PM monitoring

Blood pressure and pulse ox

Tunnel FET

Low-power Antenna

Low-power SoC

Biocompatibility, Wearable integration and packaging

Data collection, testing and analyzing, and modeling

Health and Environmental Tracker (HET)

Self-powered and adaptive sensing platform (SAP)

Tunnel FET DC converter Tunnel FET SOC

Si-CMOS based SOC TFET based SOC

Small form factor antenna

FUND

AMEN

TAL

ENAB

LESY

STEM

Glucose and lactate monitoring Non-invasive ISF extractionEnvironmental gas monitoring

EKG sensor and pulse-ox Adaptable system platform

Environmental gas monitoring

Multimodal sensor platformGlucose and lactate monitoring

Continuous monitoring platformNon-invasive ISF extraction

Revolutionary compliance platform

EKG sensor and pulse-ox

Self-powered cardiac sensingBlBloodd pressure a dnd p lulse ox

Self-powered bioelectric platformAdAdap bltable system lpla ftform

Multi wearable Self-powered Platform

multifunctional antenna

182015 2017 2019 2021

Barriers/Research Goals: • Extracting sufficient power and

robust EKG signal from arm • Validation and debugging of high

complex SoC• Long term placement• Low Power data storage• Data correlation/algorithms

Barriers/Research Goals: • Multimodal harvester > 1mW in

wearable form factors • Ultra low power blood pressure and

pulse-ox • Low power on-chip data processing• Maximizing functionality and

minimizing power levels• Data correlation on wearable node

Mesh network and connectivity between multiple wearable and fixed infrastructure nodes

Barriers/Research Goals: • Context aware adaptable system • Converting data to information• Self-powered wearables and

infrastructure sensors• On node processing capability

Vigilant Vitals Watch/PatchVigilant EKG Shirt/Armband

Wearable and IoT

Vi il t Vit l W t h/P t hVigilant EKGVigilant EKG

Self-Powered Adaptive Platforms (SAP)

192015 2017 2019 2021

Correlation of health and environmental sensors and actionable data

Barriers/Research Goals:• High energy density storage• Low power gas sensors• Biocompatibility of electrodes Sensor

selectivity/sensitivity• Low power SoC and radios• Data correlation and user feedback

Barriers/Research Goals: • Exploration of ISF extraction non-invasively and at low

power• Continuous, repeatable and specific measurements of

targeted drugs (need to be identified)• Data correlation and user feedback

Generation of a sophisticated wellness picture by measuring glucose and lactate parameters non-invasively, continuously and long-term.

Barriers/Research Goals: • Extraction of sweat from skin• Exploration of ISF extraction • Continuous, repeatable and specific biomarker measurements • Low power physiological sensors such as pulse-oximetry and BP• Long term placement on skin• Data correlation and user feedback

Medication Compliance

Glucose/LactateAsthma Platform

A revolutionary compliance detector that closes the loop on drug intake and provides real time drug efficacy and interactions

Ast Glucose/LactateGl /L t thma Platform

Health and Environmental Tracker (HET)

Feedback on Thrust Research: Friday @ 11am

Breakout session led by Thrust LeadersFast paced activity involved industry members moving from Thrust to Thrust and giving feedback, networking, discussing etc.Ensure that every industry member is exposed to all of ASSIST’s research

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Susan Trolier-McKinstry

Ben CalhounSuman Datta

Omer OralkanJess Jur

Vijay Narayanan

Sustainability Roundtable: Friday @ 10:30am

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Dr. Mehmet Ozturk, Deputy Director of ASSIST

ASSIST creates IP as patents,

copyrights (software/layouts),

and know-how

Recent disclosures

Flexible thermoelectric generators

Body-area networks and

antennas

Room temperature ozone sensing

Sweat analytics with osmotic

pumps

Smart textiles with integrated ECG22

ASSIST Intellectual Properties (over 40 IP to date)

ASSIST creates IP as patents,

copyrights (software/layouts),

and know-how

Recent disclosures

Flexible thermoelectric generators

Body-area networks and

antennas

Room temperature ozone sensing

Sweat analytics with osmotic

pumps

Smart textiles with integrated ECG23

ASSIST Intellectual Properties (over 40 IP to date)

ASSIST Students are impacting industry List of Ph.D. Graduates (2016-2017)

Francisco Suarez, Ph.D., NCSU now at FlexSaba Emrani, Ph.D., NCSU: now at SASRita Brugarolas Brufau, Ph.D., NCSU, now at IntelRahul Pandey, Ph.D., PSU, now at IntelMargeaux Wallace, Ph.D., 2106, PSU, now at GEOluseyi Ayorinde Ph.D.2016 US ARLChunhui Chen, Research Scientist II, Composites and Polymer Engineering Laboratory (CAPE), South Dakota School of Mines & TechnologyYong Hao, postdoc at FIU (looking for job)Aparajita Singh, FIU, IntelJairo Nelson finishing MS, employed by Intel, PortlandDr. Pandiaraj Manikam, Central electrochemical research institute in India

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Student Research Pitches: Thursday @ 2:15pm

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- Hong Goo Yeo

Richa AgrawalSohini RoyChoudhury

- Murat Yokus

Luis Lopez Ruiz Laura Gonzalez- Steven Mills

Raj Bhakta

- Taiwei Yue

James Dieffenderfer

SAP and HET Testbed Demos and Research Poster Session: Thursday @ 2:35pm

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ASSIST Industry Members

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Affiliate Members

Associate Members

Full Members

Coming Soon:

Industry member engagementLeverage $4M per year from NSF Engage with ASSIST researchersSteer the Center’s research portfolioGet exposed to latest cutting edge researchOpportunities to license IP coming from ASSISTHire student interns and graduated students Participate in two face to face meetings per year (May 16-17th, Miami)Sponsor research projectsWrite joint grants to federal agenciesHelp build partnershipsHelp in Testbed Demonstration

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SSSTTTTTTTTTTTTTTTTTTTTTTT

Dr. Casey BoutwellASSIST ILO

Future Roadmapping: ASSIST WorkshopsSkin for Engineers - Jan 16’ @ FIU Generating the IoT Roadmap Feb 16’ in Raleigh, NC

Standards on Wearable Tech. & E-Textiles – Mar 16’ @NCSU• ASSIST organized four NSF workshops on critical topics in wearable health technologies w/NSF supplement

• Bring together a diverse group of stakeholders from academia, government and industry

• Identify key research challenges and opportunities for interdisciplinary collaborations

• ASSIST attended CES and IDTECHEx. 29

ASSIST Bay Area ShowcaseDecember, 2016

Scientific, Medical and Military Advisory Board meetingEmphasis on recruitment, retention and engagement2 hour showcase for new prospective membersFitbit, Verily, Flextech, Fujifilm, Lockeed Martin, Striiv, Band of Angels, Matrix, Life Science Angels, MaximThanks to Profusa for hosting!

3030

CES 2017

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“Wearables” everywhereStep Tracking = DONEOptical Heart Rate = Saturating

Striiv – optical HR wearable

A few unique technologiesPKvitality – first to show microneedle monitors

K’Track GlucoseK’Track Athlete (lactate)

InBody claiming wearable with ambulatory BP measurementOptical HRTwo electrodes on wrist, two touch electrodes on top of unit for alternate hand

Energy harvesting still only solar & foot strike

Meet our ASSIST People

Row 1: Scott Ashby (Accounting Manager), Dr. Casey Boutwell(Industry Liaison Officer), Roy Charles (Diversity Director), Malakai Erskine (Administrative Director), Callie Kimberly (Administrative Support Specialist)Row 2: Rajinder Khosla (Senior Technical Advisor), Ember Melcher (Communications & Events Coordinator), Jason Strohmaier (Chief Systems Engineer), Dr. Elena Veety (Academic Director)

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We need your helpTo provide feedback on ASSIST’s systems, applications and technologies in all areasTo provide guidance on future directions for ASSIST in order to sustain itself after Year 10To help ASSIST strategically grow its industry membershipTo help commercialize most relevant technologiesTo help with clinical validation of ASSIST SystemsTo help ASSIST develop partnerships for its data strategyTo help market ASSIST’s value proposition

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Need your feedback!Sustainability/Industry Membership

How far do we go down the wearable path in the next 5 years? What are the sustainability pathways forward beyond Year 10?We have numerous collaboration opportunities: when should we say NO?

CommercializationWhich are the most commercializable technologies from ASSIST ? Are there any you’d like to co-develop?Which technologies do Industry members find the most compelling for spinoffs and startups?

DataWhat clinical data do the clinicians want and how should they receive it?How do we engage with companies, clinicians, etc. once we have mature Testbed devices? How do we get a defined benefit out of data generated from our devices?What would be the data handling strategy given the limited funding and need for data correlation/causation studies and for providing user feedback?

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Need your FeedbackSystems and Clinical Impact

Are the current systems and platforms delivering on what clinical needs are? How relevant are the current use cases?What would be the technology readiness level (TRL) strategy when defining collaborations and clinical studies (proof of concept vs. randomized clinical trial, unsupervised vs. supervised experiments)?

Technology Needs (sensors, materials, textiles etc.)Drug compliance : How should we approach this? What other sensors would you want in our systems?What should our strategy be in textiles. How can we maneuver this toward future cases? With regards to gas sensors, what else are we interested in exploring in the environment? What about breath?With regards to photoplethysmogram sensors, are there any other cardiovascular health indicators we should explore besides cuffless blood pressure by pulse transit time?

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Thank you

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ASSIST-ing Industry: Engagement and Support

Casey Boutwell, Ph.D., MBA

Director of Industry Engagement

Casey_Boutwell@NCSU.edu (919) 515-3083

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ASSIST Industry Engagement: Who is Casey?

Advocate for Industry’s needs and interests in ASSIST (across universities)

A resource for students, faculty, and staff (for all things “industry”)

Professional background in IP strategy and license negotiation

Research background in optical sensing/semiconductor materials

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Industry LiaisonCasey Boutwell, Ph.D., MBACasey_Boutwell@NCSU.edu(919) 515-3083

ASSIST Industry Members

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Affiliate Members

Associate Members

Full Members

Coming Soon:

ASSIST Supports Industry

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Connecting Engineers, Supporting Collaboration Send your engineers to ASSIST workshops and conferences

Incorporate ASSIST inventions and discoveries in your internal R&D

SAS Institute

Working with ASSIST to build analytics on ECG-integrated, fitted garments

Teamed with ASSIST Textiles and Electrical Engineers, and SAS Data Scientists

(You’ll hear a lot more at lunch!)

Profusa, Inc.

VC-backed, Bay Area medical device startup, with experienced management team

Partnered with ASSIST on multiple federal grant awards

(Newest ASSIST Member!)

Disseminating Knowledge Student/Industry Webinar, recorded WebEx for Industry Members

Focused on state-of-the-art energy harvesting

Broad participation among partner schoolstudents

Accessing Faculty Expertise Internationally recognized leaders in their fields

Broad experience with industry funded research

Access to top-quality facilities

Laboratories with hundreds of highly skilled students and professional research staff in their sphere’s of influence

ASSIST Supports Industry

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Industry Engages ASSIST

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Directly Sponsoring Research

Sponsor a project with competitive commercial applications

Negotiate exclusive commercial rights directly from researching university

Pick the team and direct the focus to your needs

Projects can last from months to years (longer if you hire the students!)

ASSIST experience with Materials, Textiles, Chemicals, Systems, Biological

Sciences, Devices, Data Analytics, Design, and much more

Industry Engages ASSIST

43

Supporting Senior Design ProjectsSponsor undergraduate or graduate student teams

Direct students to explore specific applications of interest

Support their work towards a proof-of-concept device

Acquiring Talent Directly incorporate the ASSIST culture of cross-disciplinary collaboration

Access not only to solutions, but to our problem solving processes as well

Recent Commercialization Activity

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Three commercial licenses to startups in last 8 months: BioMindR (hydration monitoring with RF), VieMetrics (1st

ASSIST spinout), XYZ Corp. (in stealth mode)

ASSIST supporting I-CORPS Site Proposal at NC StateTravel

Close coordination with tech licensing/new ventures offices

InterviewsEducation

Emerging IP Opportunities

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Direct-write Printed Heaters and Electrical Vias for TextilesOptically Transparent Ultrasonic Transducer ArraysFlexible Thermoelectric Devices using Liquid Metal

~40 Invention Disclosures, >dozen patent filingsConfidential details in tomorrow’s Advisory Board Meeting

Technology Leadership

Follow projects related to your business

Gain access to expertise, facilities, and IP

Strategic Co-opetition

Build partnerships with ASSIST Members

Leverage company resources

Developing Talent

Evaluate students as employees

Establish relationships with ASSIST faculty

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ASSIST-ing Industry: Member BenefitsFull Members

Priority IP rights

Full voting power (on project

selection, funding, and IP strategy)

Associate Members

Secondary IP rights

Voting power (on project selection,

funding, and IP strategy)

Startup Members

Access to all ASSIST workshops and

conferences, students, and faculty

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Questions at break please

Director of Industry EngagementCasey Boutwell, Ph.D., MBACasey_Boutwell@NCSU.edu(919) 515-3083

Testbed: Self-Powered and Adaptive Platform (SAP) OverviewJohn Lach – SAP Testbed LeaderUniversity of Virginia

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3-Plane Diagram

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Systems-Driven Research

Testbeds should1) Provide proof-of-concept demonstrations that the Center’s

fundamental scientific and technological barriers are being overcome and the Center’s vision is being realized,

2) Drive the Center’s fundamental research directions through top-down strategic planning, project selection, technology assessment, and specification refinement, and

3) Provide a common framework for technical discussions, education, and cross-group collaboration stimulation.

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Testbed Definition Process

51

Regular interaction

Model-based Gen 0 (COTS)Gen 1Gen 2Gen 3

52

Unique game changing ASSIST Technologies

Enabling ASSIST technologies for Testbed

COTS or External Collaboration

V. Wearability and Data

IV. Low Power System-on-Chip

III. Low Power Wearable Sensors

II. Low Power Emerging Nanoelectronics

I. Energy Harvesting and Storage

ASSIST Research Thrusts Health & Environmental Tracker

Self-Powered Adaptive Platform

SAP Gen i = self-powered HET Gen i-1

Self-powered, wearable ECG and accelerometer sensing and wireless streaming

Strain harvester on chest (>500uW target)Harvester: Rahn

Conversion and supercap interface: Kiani,Rajagopalan/Randall

Modular ultra-low-power electronics architecture

Architecture: Lach, Calhoun, Bozkurt

Custom electronics: Calhoun, Wentzloff,Werner, Lach

Physical integrationPolar-strap-like device: Rahn, Lach

Connection to ECG shirt electrodes: Jur

Current Gen SAP

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Self-powered, wearable ECG and accelerometer sensing and wireless streaming

Strain harvester on chest (>500uW target)Harvester: Rahn

Conversion and supercap interface: Kiani,Rajagopalan/Randall

Modular ultra-low-power electronics architecture

Architecture: Lach, Calhoun, Bozkurt

Custom electronics: Calhoun, Wentzloff,Werner, Lach

Physical integrationPolar-strap-like device: Rahn, Lach

Connection to ECG shirt electrodes: Jur

Current Gen SAP

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Power for more functionality

Sub-system Research for Future SAP Gens

Sub-system-hTEG harvesting on wrist:Vashaee, Ozturk

w/ converter: Calhoun, Lach

Sub-system-mMechanical harvesting on elbow: Trolier-McKinstry, Roundy

w/ converter: Kiani

Sub-system-nvp:Intermittent-powered sensing platform with non-volatile processor: Narayanan

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SAP RoadmapDemonstrate and deploy SAP 1 in human subject studies

Functionality, application utility, human factors, etc.SAP 2 = self-powered HET 1

Chest platformStrain harvester on chest (>1mW target)Modular electronics architectureMulti-modal sensing (all HET 1 chest patch modalities)Textile integration

Wrist/elbow platformTEG and/or mechanical harvesting on wrist/elbow (>1mW target)Modular electronics architectureNon-volatile processing for intermittent powerMulti-modal sensing (all HET 1 wrist modalities)

SAP 3 = self-powered HET 2Include biochemical and PM(?) sensing

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SAP Roadmap

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Regular interaction

Model-based Gen 0 (COTS)Gen 1Gen 2Gen 3

SAP Featured Project: Mechanical Energy Harvesting from Wrist and Upper Arm MotionDr. Shad Roundy (University of Utah)

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Two Goals – Two Approaches

Initial Target: 50 W from wrist under walking conditionsStretch Goal: 100 W

Target: 2 mW from elbow joint motion under walking conditions.(Average angular velocity of 90 /s)

1/27/2017 59

Wrist Worn Device

• Central hypothesis: More of the available mechanical energy can be captured by lowering the mechanical losses (i.e. damping) and optimally designing the level of electromechanical coupling from the transducer

1/27/2017 60

z

Y

XZ

x

y

= 12

Seiko watchOscillating

weight

Gear train

Generating rotor

Generating coil

Capacitor / Battery

Mechanical Power Available

1/27/2017

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

2 4 6 8 10

Pow

er [μ

W]

Rotational Inertia [10-7 kg m2]

Wrist - Walking

Kinetron

Seiko

Our Approach: Magnetically Plucked Piezo Generator

1/27/2017 62

Piezo beams

Rotor

Magnets

Flexible circuits1st Generation 2nd Generation

3rd Generation

• < 40 W Pseudo walking• 3/12 working electrodes• Fragile / difficult assembly• 12 mm thick

• Poor power output• 6/12 working electrodes• More robust

• 42 W Pseudo walking• 9/12 working electrodes• More robust• 8.3 mm thick

Prototype and Test Results

1/27/2017 63

Custom fabricated thinfilm PZT

System design, and testing

New “In-plane” Design

64

PCB Beam Hub

Brass rotor Tungsten weight

PZT beams

Magnets Shaft Casing

BearingsDesign Petal In-plane

Number of beams 6 6

PZT volume 7.8 mm3 2.7 mm3

Normalized strain* 1 3.2

Normalized Power 40 W† 120 W

*Based on static FEA result†Based on the best electrode

New “In-plane” Design

1/27/2017 65

1/27/2017 66

Elbow Worn Harvester

Target: 2 mW from elbow joint motion under walking conditions.(Average angular velocity of 90 /s)

1/27/2017 67

Why Elbow Joint Motion?• Heel strike, knee, and center of

mass motion would not address ASSIST needs

• Hip and shoulder motion are multi-degree of freedom –harder to couple

• Elbow motion is energetic enough, fits with ASSIST objectives, and is single degree of freedom

• System can be scaled up (i.e. ankle motion) or down (i.e. finger motion)

1/27/2017 68

Riemer and Shapiro, Journal of Neuroengineering and Rehabilitation, 2011.

Elbow Joint Harvester Design

Piezo beam mechanisms

Rotor creates frequency up-conversion

Power Estimates

• This may overestimate the actual power we will get• Indicates that > 1 mW is within reach

Parameter ValueLength x Width 30 X 3 mmPiezo thickness 3 um on each sideNumber of beams 4Elbow rotation rate 90 deg/secMax beam strain 0.15%Max power estimate [mW] 1.2

= = 2

Power Estimates

1/27/2017 71

Path Forward

• Final prototype and characterization of wrist worn harvester with PZT from Prof. Trolier-McKinstry’s group

• Elbow motion harvester• Integrate PZT from Prof. Trolier-McKinstry’s group• Integrate with power electronics from Prof. Kiani’s group• Revise design to scale down size and thickness

1/27/2017 72

Acknowlegements

1/27/2017 73

Graduate students:

Xiaokun Ma, Miao Meng, Tiancheng Xue,Hong Goo Yeo

Faculty:

Tom Jackson, Mehdi Kiani, Chris Rahn, Shad Roundy, Susan Trolier-McKinstry

SAP Featured Project: Integrated Sensor Node Design and Prototyping

IAB MeetingJanuary 26, 2017Ben Calhoun (UVA)

3-Plane Diagram

75

Testbeds: SAP (and HET) – Thrust IV: IC design

Environmental Sensor

HET Testbed

Energy Harvesting

SoC

RADIO

Analog Front End

Power Management

SoCDigital Control /

Processing / Management

Energy Storage Antenna

SAP Testbed

Medical / Off Body

Bioc

ompa

tibi

lity

Software

Physiological Sensor

COTS

Add

-on

Physiological Sensor

SoC

RADIO

Analog Front End

Power Management

SoCDigital Control /

Processing / Management

Battery

Antenna

Software

Aggregator

Cloud Storage

Signal Processing

User InterfaceSignal Processing

SmartphoneIOIO

Radio

76

SAP Gen 2 Approach: Multi-chip solution

Circuits and Systems for Gen 2Testbed system drivenMulti-chip approach

Implement long-term strategy from Year 3Chip-chip interfacesEnergy harvesting / power managementCore system platform

77

SoCRADIO

Analog Front End

Power Management

S CSoCDigital Control / Processing / Management

Antenna

Software

NVM

Central SoC:target <1μW

External NVM: better system operation

External RFIC and antenna

External sensor interfaces for

faster upgrades

Chip 1

Chip 2

Chip 3(UM)

Chip 4

Next Generation SoC Based System

78

Better system operationOff chip module support Multi-chip platformNew power management unitImproved signal and data paths, lower bus useGeneral Purpose In/Out communication

SoC taped out in August 2016, ahead of schedule

Collaboration: Calhoun (UVA), Wentzloff (UM)

Next Gen SoC Power Delivery

79

Testbed Needs:Higher VDD for COTSLower IDDQ (nW) for low ILOAD(<5μW)

HarvestStorage

1V analog rail

0.4-0.55V digital rail

Regulators

1.8V rail for external components

TEG

SOLAR

Next Gen SoC Power Delivery

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Results:71.1 % end-end (EH+PMU) efficiency1.3 nW gate leakage reference400 nW IDDQ

HarvestStorage

1V analog rail

0.4-0.55V digital rail

Regulators

1.8V rail for external components

TEG

SOLAR

Ultra-low Power Wired Communication

81

Testbed Need:Multi-chip solutionBenefit from different logic technologies

Proposed Solution:Separate Buses for RF, NVM

Allows independent communication between chips

Differential signaling for noise immunity3.77 nW, 11.4 fJ/b/mm in textile

Collaboration: Calhoun (UVA), Jur (NCSU)

Nonvolatile Memory for Energy harvesting SoCs

82

Testbed Need:Varying harvesting conditions Power and data loss.

Proposed Solution:Non-volatile memory (TI FeCap) optimized for read at startup.Small NV FIFO for critical data at power down.

DPM in Main SoC controls Boot up and Back up sequences based on available energy

nvChip is completely powered off after backup/bootup.TAPED OUT - April

g g

Collaboration: Calhoun (UVA), TI

Next Generation SoC Architecture

83

Integrated ECG sensing (150nW)Interfaces to NVM, BLE TX, GPIO, ECGProcessor (LCU) and memoryIntegrated EH-PMU (from earlier slides)Accelerators: FIR, MAC, timers, lossless compressions in series with BLE TXnW level XTAL

Collaboration: Calhoun (UVA), Wentzloff (UM)

Next Generation SoC Results

84

507 nW active power for SoC

MCU, IMEM, SPI, IO, Timer, GPIO, XTALMultiple memory modes give flexibility

Sends data via BLE TX (UM)Interfaces to accelerometerExample jolt/fall algorithm application fully functional in system

Collaboration: Calhoun (UVA), Wentzloff (UM)

This work ISSCC15 ISSCC14 ISSCC15 UVA Gen 1 SoCBattery-less Yes No No Yes YesHarvests power Yes No Yes Yes YesFully integrated EH-PPM Yes No No No NoPowers off-chip Sensors Yes No No No NoRegulated voltages

1.8V,1.0V,0.5V

0.25V-1.2V - unregul

ated1.2V,0.5V,variable

Interface to NVM Yes No No No No

On-chip SRAM 4KB 24KB 3.7KB 256B 12KBAccelerators 5 2 3 - 7Sensing interfaces 3 2 1 - 2Total Power 507nW 850nW 45nW 295 pW 2.3μW

Components Included in Total Power

MCU + SPI + IO + Timer + GPIO + RI

MCU AFE + DSP MCU MCU + IO +

SPI + FIR

Task Next Steps

85

Improve system integration; Testbed demosInterface to more chipsBroaden applicationsSeek industry guidance to expand platformContinue setting best in class IC results

Questions?

Selected Next Gen SAP Testbed SpecificationsSelected Testbed Specifications

Item Spec Typical Units

SoC, incl. PMU

Power

1 μW

ECG Sensor + ADC 150 (down from 3000) nW

NVM 160 nW

Accelerometer 2 μW

Total System <5 μW

BLE compatible RF

TX/RX Power 400 / 200 (down from 10,000) μW

Standby Power <2 μW

RX Sensitivity -80 dBm

AntennaTx/Rx Frequency 2.4 GHz

efficiency 80 %

SRAM array leakage 5 (down from 3000) nW87

SAP Featured Project: Bulk Nanocomposite Thermoelectric Materials

Dr. Daryoosh Vashaee (North Carolina State University)

88

Thermoelectric Generators for Body Heat Harvesting

Students: Abhishek Malhotra, Michael Hall, Amin Nozariasbmarz, Francisco Suarez, Yasaman Sargolzaeiaval,Viswanath Padmanabhan Ramesh

Postdoc: Jie Liu

PIs: Daryoosh Vashaee, Mehmet Ozturk

ASSIST platform

90

SmartphoneWearable Node

Body Energy

Gas Sensor

SoC

RADIO

Analog Front End

Power Management

FFFF tt

mmmenm t

Digital Control / Processing / Management

Energy Storage Antenna

Data Aggregator

Signal Processing

Health Sensors

BoEne

Software

PN

Epidermis

Dermis

Hypodermis

PNNNNN PPPPP

37 0C

20-25 0C

A Thermoelectric Generator (TEG) can convert body heat directly into electric power.

While the temperature difference between the body and the ambient is about 10 – 15 degrees, very little of this drops across the TEG

Two reasons:Large temperature drop across the TEG air interfaceLarge temperature drop across the skin –essentially a thermal insulator

37 0C

25 0C

Epidermis

Dermis

Hypodermis

PNNNNN PPPPP

37 0C

20-25 0CAir

TEG

Skin

Temperature across the Skin and TEGMost of the temperature drops across the air and the skin:• 2.5 degree across the skin• 0.7 degree across TEG• 13.8 degree across air

Assume: Natural convection, Ta=20 oCH = 1mm (TE leg height)

Z

Temperature across the Skin and TEGThe skin is ~0.5 degree cooler under the TEG.

Skin area under TEG

Z

x

Nanocomposites for Improved Thermoelectric Performance

Alloyed Powder Preparation Crystallite Size Reduction Ingot Preparation

MechanicalMilling

InductionMelting Mechanical Milling Hot Press

96

Rigid TEGs Flexible TEGsEGaInPDMS

TE Legs

Solder+Wire

1mm

Thermal Compression Bonder

TEG Packaging

Material Characterizations

Measurement of Electrical conductivity, Seebeck coefficient and ZT from -130C to 1500 C.

Equipped with all critical equipment for TE materials characterization

Laser Flash and DSC systems enable thermal conductivity measurements.

98

Metronome

TEGs are characterized versus their mechanical & thermoelectric propertiesOn body measurements are performed for the actual power generation.

Device Characterizations

Comparison of the TEG Power on Wrist, Upper arm, T-shirt and Chest

99

Air velocity (m/s)

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Pow

er (

W/c

m2

)

0

5

10

15

20

25Wrist

Upper ArmT-shirt

Chest

Top spreader on the TEG

Load resistor

Top spreader

Load resistor

Oscilloscope probes

PDMS

Tape

Melissa Hyland, Haywood Hunter, Jie Liu, Elena Veety, Daryoosh Vashaee, Applied Energy, 182, 2016, 518–524

100

Daryoosh Vashaee, Amin Nozariasbmarz, Lobat Tayebi, and Jerzy S Krasinski, U.S. Provisional Patent Appl. No. 62/260,829 (2015)

5

10

15

20

25

0 10 20 30 40 50 60 70

Cry

stal

lite

size

(nm

)

Exposure time (s)

Nanostructures made by microwave radiation showed unusually small thermal conductivity!

Comparison with Commercial TE Devices

101

Voc

(mV/cm2)Isc

(mA/cm2)Pout

( W/cm2)COTS 18.4 1.5 5.7 No airflowCOTS 52.9 3.2 35.5 With AirflowNano 49.7 3.9 44.2 No airflowNano 97.4 7.1 156.5 With Airflow

Used 14.3 cm2 spreader on both sides.

Nano COTS

10

Air flow

Air flow

No Air

No Air

COTS NanoSuarez et al., Energy & Environmental Science, 2016, DOI: 10.1039/C6EE00456C

102

Students and postdocs: Abhishek Malhotra, Michael Hall, Jie Liu, Amin Nozariasbmarz, Koushik Devarajan, Haywood Hunter, Yasaman Sargolzaei, Viswanath Ramesh, Francisco Suarez, Haywood Hunter, Payam Norouzzadeh, Zach Coutant, Aditi Agarwal, Runze Liu, Melissa Hyland

Technical Assistant: Henry Taylor

TEM images: Dr. James Lebeau’s team

Sponsor: NSF (EEC-1160483), AFOSR (FA9550-12-1-0225)

Acknowledgment

Thank you!

Break

104

Testbed: Health and Environmental Tracker (HET) Overview

Dr. Alper Bozkurt, HET Testbed Leader (North Carolina State University)

105

106

Dr. Alper Bozkurt (HET Leader)IAB Meeting

January 26th, 2017

106

TESTBED: Health and Environmental Tracker (HET) Overview

107

Unique game changing ASSIST Technologies

Enabling ASSIST technologies for Testbed

COTS or External Collaboration

V. Human Factors and Data

IV. Low Power System-on-Chip

III. Low Power Wearable Sensors

II. Low Power Emerging Nanoelectronics

I. Energy Harvesting and Storage

ASSIST Research Thrusts Health & Environmental Tracker

Self-Powered Adaptive Platform

2015 2017 2019 2021108

ASSIST VISION: Correlation of health and environmental multimodal sensing data leading to intelligent action

ASSIST Application: Exposure related respiratory health

ASSIST VISION: Generation of a sophisticated wellness picture by measuring biochemical parameters non-invasively, continuously and long-term.

ASSIST Application: Glycemic index management ASSIST VISION: Creation of a

revolutionary compliance detector that closes the loop on drug intake and provides real time drug efficacy data and drug to drug interaction

ASSIST Application: Medication efficacy monitoring and medication dosage adjustment for personalized medicine

Evolution of Nano-Enabled Health and Environmental Tracker Testbed

HET Gen-0 to Gen-1 transition

109

ASSIST CustomTechnologies

Antenna

Power Power Management

SPI

RRAADDIIO

Battery

Sensors

SPIMSP SOC

SBreakout Board

Antenna

Power Power Management

SPI

RRAADDIIO

Battery

Sensors

SPIMSP SOC

SBreakout ut Board

CHEST PATCH w/ breakout sensor boards

WRISTBAND w/ breakout sensor boardsWRISTB

CustomizedOff the Shelf Technologies

GEN-0

Antenna

Power PowerManagement

SPI

RRAADDIIO

Battery

Sensors

SPIMSP SOC

SBreakout Board

Antenna

Power Power Management

SPI

RRAADDIIO

Battery

Sensors

SPIMSP SOC

SBreakout utut Board

CHEST PATCH w/ breakout sensor boards

WRISTBAND w/ breakout sensor boardsWRISTB

GEN-1

Power Benchmarking

110

ASSIST CustomTechnologies

CustomizedOff the Shelf Technologies

1 10

100mW

Skin Imp.Skin Im(36

mp.in Im66 mW

mp.WW)

System on ystem oChipChip

(11.5 pChip

55 mWWW)

ECG(0.5

GECG55 mWWW)

Pulse OxPulse(15

Oxulse55 mW

OxWW)

Chest Patch(0.55 mWmW)

Pulse Ox

( )Accelerometer

P l

Accelerom(0.06

O

mete

O

erom66 mW

ermeteWW)

((( )))MicrophoneMicroph(0.35

honeoph55 mW

oneWW)

System on ystem oChipChip

(11.5 pChip

55 mWWW)

AccelerometerAccelerom(0.06

meteerom66 mW

ermeteWW)( )

Ozone SensorOzone Se(102

ensoe Se2 2 mW

ornsoWW)

Humidity/Humidity/Temp SensorTemp Se

(0.45 nsorp Se

55 mWrnsor

WW)

Humidity/

Pulse OxPulse(15

Oxulse 55 mW

OxWW)

Wrist Band

1 10

100mW

System on ystem oChipChip

(0.03 pChip

33 mWWW)

AccelerometerAccelerom(0.06

meteerom66 mW

ermeteWW))(

Ozone SensorOzone Se(0.15

ensore Se55 mW

rnsorWW)

Humidity/Humidity/Temp SensorTem

(mp SeTem

((0.23 nsorp Se

3 3 mWrnsor

WW)

Humidity/

PPGPPG(0.43

PPG33 mWWW)

Wrist Band

GEN-0

Skin Imp.Skin Im(0.06

mp.n Im66 mW

p.WW)

System on ystem oChipChip

(0.03 pChip

33 mWWW)

ECG(

ECG(0.05

ECG5 5 mWWW)

PPGPPG(0.05

PPG55 mWWW)

Chest Patch( )

PPG

AccelerometerAccelerom(0.06

PP

mete

PP

erom66 mW

er

G

meteWW)

( )Microphone(

crophMic((0.42

honeoph2 2 mW

oneWW)

GEN-1 1 10

100mW

1 10

100mW

Modular HET Gen-0 System

111

Wristwatch• Three axis accelerometer• COTS Ozone sensor• Custom Ozone sensor• Temp/Humidity sensor• Built-in for pulse ox

Chestpatch• Three axis accelerometer• Single channel ECG• Built-in for pulse ox• Wheezing microphone

Next Version of HET Gen-0 Watch

112

New Features:• Improved temperature/humidity sensing• Color e-paper screen• Plug-in “cartridges” for ASSIST sensors that can

include additional circuitry (to accommodate VOC sensors)

• 512MB of flash memory• Syncs with smartphone via Bluetooth when prompted

(rather than continuously streaming data via Bluetooth)

• Integrated pulse-oximetry (red, IR, and green light)• Improved power management• Inductively charged (simply place on charging mat)

Medical gradeECG electrodes

Screen printedsilver-silver chloridetextile electrodes

Stretchable silver nanowireelectrodes

113

HET 1.0 Status

HET 2.0

114

1cm

Lactate Sensor

Potentiostat Watch

Evaporation Pad

Encased Enccased PaperPaaper

Channel

Electrodes

SamplingSammplingFluid

115

Fixed Chemistry / Swelling of Microneedles without Dissolution

Dry Swollen

Dry Swollen

HET 3.0

Data Generation Efforts

116

1. Stability/accuracy/sensitivity of sensors during various activities and use of the sensors for activity recognition and estimation of minute ventilation (ASSIST space and UNC-EPA facilities)

2. Effect of ozone and VOC on heart rate and heart rate variability, respiratory rate, spirometry and wheezing (UNC-EPA facilities)

3. Use of dual PPG (chest and wrist) and ECG to obtain pulse transit time and effect of this in the accuracy improvement of blood pressure prediction (ASSIST space and UNC-EPA facilities)

4. Effect of stress on heart rate, heart rate variability and pulse transit time (UNC Psychiatry)

5. E-shirt based data collection (industry collaborator)

Prototype Centered Resources

117

An open and customizable platform for correlated sensing of health and environment (wearable devices as IOT nodes)Connection with the research on wearables for vital sign monitoring, biochemical sensing using sweat and interstitial fluid, medication compliance and interactionUnique nano-enabled technologies for lower power consumption Data for determining the causation between the health outcome and the environmental factors and predicting the exacerbations for self-management of wellnessExperimental space (environmental chambers, instrumented exercise rooms, etc.) for data collectionNetwork with federal and state agencies and being involved in standard and roadmap developments

HET Featured Project: Low Power Pulse Oximetry

Dr. Alper Bozkurt, HET Testbed Leader (North Carolina State University)

118

HET Featured Project:Low Power Pulse OximetryPI: Alper Bozkurt and Michael DanielePostdoc: Vladimir PozdinStudents: Peter Sotory, Jose Sarmiento, James Dieffenderfer

119

Pulse Oximeter Power Reduction

120

tissue-device coupling modelling

multi-junction or organic devices

anti-reflective coating

wavelength selection

monolithic TIA

compressed sensing

Tissue LEDPhotodiode

Micro Controller

BlueTooth LE

WiFi

Accelerometer

Energy Harvester

AFE

duty cycling

previous years

this year

System Architecture

• Compressive Sampling reduces LED power proportional Compression Ratio (CR = N/M) (8x, 10x and 30x)

• Challenges – Signal/Feature recovery

121IEEE ISSCC 2016 and IEEE TBioCAS 2017

ASIC Overview

• 4.0mm x 2.5mm, 180nm CMOS process• 1P6M, 8kATM, 2fF/μm2 MIM, HRP

• 1.2V operation

158.8μW122 of 39

6μW7.2μW

Performance ComparisonThis Work TBCAS’10 [8] ISSCC’13 [9] TBCAS’08 [10] TBCAS’15 [11]

Tech. &Supply

0.18μm, CMOS1.2V

1.5μm, BiCMOS5V

0.18μm, CMOS0.5V

0.35μm, CMOS2.5V

0.18μm, CMOS1.8V

Sampling Frequency

128, 16, 13 and4Hz 100Hz 32kHz 100Hz 165Hz

DC Current Cancellation Up to 10μA NR Up to 4μA 53.6μA (Ext HPF) 100μA

Integrated Noise (RTI) 486pArms

* NR NR 2.2nArms 600pArms

Noise Bandwidth 10Hz NR NR 6Hz 10Hz

Integrated Feature

ExtractionYes (HR/HRV)

Data Compression

Yes (8x, 10x and 30x)

Power Consumption

(Readout)172μW

Power Consumption (LED driver)

1200-43μW 4400μW NA (Ambient light) NR 1125-120μW

0.0.0.0.0.00.0000.0.0.0.000.000..00 181818181818181881818118111818188181118888111811 μmμmμmμmμmμmμmμmμμmμmμmmmμmμmμmμmμμmμmμmμmμμμmμmmμμ ,,,,,,,,,, CMCMCMCMCMCMCMCMCMCMMCMMCMCMCMCMCMCMCMMMCCMMCMMMCMMMMCMMMMMMOSOSOSOSOSOSOSOSOSOSOSSOSOOSOSOSOSSSOOSOOSSSOSSSSOOOOSOS1.1.1.1.1.1.111.11.1.1111.1.111 2V2V2V2V2V2V2V2V2V2V2V222V2V2V2V2V2V222VV2V22V2V2V2V

1212121212112122121212112112121222222122122212228,88,8,8,8,8,8,8,8,8,88888888,8 161616161616161661161661611161616166616116111 ,,,,,,,,,, 131313133113131313131313311333331313 anananananaaananaannnannnnannnanaa dddddddddddddddddddddddddddd4H4H4H4H4H4H4H4H4HH4H4H44H4H4HHH44H44H4HHHHHH4HHHzzzzzzzzzzzzzzzzzz

UUpUpUpUpUpUpUpUUpUUpUpUpUUUpUpUpUUUpUpUUUUpUpUpUU tttttttttttttttttttooooooooooooooooooooo 1010101010101010101011000010100000100μAμAμAμAμμμAμAμAμAμAμAμAμμμAμAAAAμμAμμμAμAμAAμμAμμμμμ

484848484848484884848484844848484484848888488844886p6p6p6p6p6p6p6pp6p66p6p6p6666p6p6pp6p6p6p6 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAArrrrrrrrrrrrmmmmmmmmmmmmmmmmsssssssssssssssssss************

10101010101010100011010101101010010110HzHzHzHzHzHzHzHzHzHHzHzHzHHHzHHzHzzzzHzHzHHzHHHHH

YYYYYYYYYYYYYYYYYYYYYYYYeeeeeeeeeeeeeeeeeeeeeeeeYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY s sssssssssss ssssssss (H(H((H(H(HH(H((H((((H(HHHH(H(H(H((H((HH((HHHRR/R/R/R/R/R/RR/R/R/R/R/R/R/R/RRR/RR/RR/RRR/RRR HHHHHHHHHHHHHHHHHHHHHHRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRV)V)V)VV)V)V))V)V))V))V)V)V)VVV)))VV))VV))))V))VVVRRRRRRRRRRR

YYYYYYYYYYYYYYYYYYYYYYYYesesesesesesesessesseseseeseesesssssesesessssssYYYYYYYYYYYYYYYYYYYYYYYYYYY (8(8((8(8(8(8(8(8((8(88888(8(88(8888888888x,x,x,x,x,x,x,x,xx,xxxx,xxxxx,x,,xxx 10101110101011010101010100001010110111001011010xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx anananananananananannnnnanaananannnnnaannnnannanand d d dddddddddd dddddddddd)))))))30303030303030303030303303030333333000x)x)x)x)x)x)x)x)x)xxxx)x)xxxx)x))))x))

711717171717171717771717717171717177771 2μ22μ2μ2μ22μ2μ2μ2μ22μ2μμμ2μμ2μ2μμ2μμ2μμ2μ2μWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWW

12121212121221221212121212221211222120000000000000000000000000000000000000000000000 -------------43434343434343434343434343434443343434444333343444 μWμWμWμWμWμWμWWμWμμWμWμWμWμWμWμWμWWμWWWWWμWμμWμμμ

* Rf = and Cf = 6pF123 of

No No No No

No No No No

400μW 4μW 600μW 216μW

IEEE ISSCC 2016 and IEEE TBioCAS 2017

ASIC Highlights• Ultra-low power highly integrated PPG readout ASIC

Compressive sampling based acquisition

Upto 30x reduction in LED driver power consumption

Integrated digital back end for feature extraction

Heart rate extraction directly from compressively sampled PPG signal without reconstruction

Wide heart rate range (30-210bpm) with 3bpm resolution

• The ASIC will enable future low power PPG based heart rate monitoring systems

124 of

On-going Work: OLED & OPD Fabrication

125

Flexible Substrate

Intermediate Layer

Emissive Layer Intermediate LayerEvaporated Metal

Light Out

Encapsulation Material

Green or RedLight

Flexible Substrate

Intermediate Layer

Photoactive LayerIntermediate LayerEvaporated Metal

Light In

Green or Red Light Refracted from Tissue

EncapsulationMaterial

Mechanically Resonant Chem/Bio Sensor Arrays Based on Capacitive Micromachined Ultrasonic Transducers

PI: Omer Oralkan

Students: Chunkyun Seok, Marzana M. Mahmud, and Ziad Ali (undergraduate student)

Contributing Students: Xiao Zhang, Oluwafemi Adelegan

NC State University

126

Volatile organic compounds are major pollutants in indoor and outdoor environments as well as in industrial settings

127

Personalized exposure monitoring is important to link the environment to the physiological state.

In industrial settings the distance between the monitoring sensor and the source of the VOC could cause measurement errors in total exposure.

http://www.theozonehole.com/badozone.htm

Commercially available VOC sensors are not suitable for use in wearable platforms

128

Usually offered as total VOC sensors, not specific to different types of VOCs.Traditional MOx type sensors require heating.Power consumption is in the tens of mW range, which is not suitable for self-powered wearable platforms.

Product Target gases Sensing principle Sensing range Power consumption

Operating temperature& response time

Datasheet

& Price

Figaro TGS-2602 Air contaminants (VOCs, ammonia, H2S, etc.)

MOS type 1 ~ 30ppm of EtOH

280 mW (typical) Roomtemperature

http://www.figaro.co.jp/en/product/docs/tgs2602_product%20infomation%28en%29_rev03.pdf

AS-MLV-P2 VOCs and CO MOS type Low ppm range 34 mW 300ºC https://ams.com/kor/content/download/686543/1787717/file/AS-MLV-P2_Datasheet_EN_v1.pdf

MiCS-5524 VOCs and CO MOS type 0 – 500 ppm ~100 mW -30ºC - 85ºC http://www.pewatron.com/fileadmin/user_upload/datasheets/sensors/e/103-21-394-004-EH-0714.pdf~ $50 (sensor & evaluation board)

MiCS-VZ-86/89 VOCs and CO2 MOS type 400-2000 ppm equivalent CO2

0-1000 ppb isobutylene equivalent VOCs

190 mW for F version (5V DC)

125 mW for T version (3.3V DC)

0°C to 50°C

< 5 secs

http://www.sgxsensortech.com/content/uploads/2015/01/Datasheet-MiCS-VZ-86-and-VZ-89-rev-6.pdf

$30iAQ-core C 70-0100 VOCs and CO2 MOS type 450 – 2000 ppm

CO2 equivalents

125 – 600 ppb TVOC equivalents

66 mW (maximum in continuous mode)

9 mW (maximum in pulsed mode)

0°C to 50°C

First functional reading after start up = 5 minutes

file:///C:/Users/mmahmud/Downloads/iAQ-core_Datasheet_EN_v1.pdf

$36/piece(min. 10 pcs)

Mechanically resonant sensors with polymer functionalization layers present an alternative way of sensing

130

Quartz Crystal Microbalance (QCM)Surface Acoustic Wave (SAW)Lamb Wave ResonatorsCantileversFilm Bulk Acoustic Resonator (FBAR)Capacitive MicromachinedUltrasonic Transducer (CMUT)

Single-crystal silicon thin plate

Vacuum gap

Silicon nitride insulation layer

Glass substrateChromium/gold bottom electrode

Basic CMUT structure

The Capacitive Micromachined Ultrasonic Transducer (CMUT)

Basic Structure – An electrostatically actuated thin plate resonatorVacuum Cavity: Higher Q than cantilever with an equivalent area.Parallelism: Multiple resonating cells in an element (Robustness) Low motional impedance.Array Structure: Multi-channel array with elements functionalized with various polymers enhance selectivity.

131

Challenges in VOC Gas Sensing in a Wearable Platform

132

Low-power consumptionLimit of detection (LOD) and resolution requirements in the tens of ppb level allows use of lower frequency devices to help lower power consumption

Insusceptibility to environmental changes, e.g., temperature, humidity, pressure

Non-functionalized reference channelHigh specificity

Multi-channel sensorDifferent polymers for each channel

Overall VOC Gas Sensing System

133

Surface functionalization

Nanoengineeredpolymers

Main Processor

Frequency to Digital Converter

Digital data

Target analyte: VOCs

0 0.5 1 1.5 2 2.5 3-4

-3

-2

-1

0

1

2

3

4

Time (mins)

Freq

uenc

y sh

ift (k

Hz)

10 ppm12 ppm14 ppm16 ppm18 ppm20 ppm

clean air toluene clean air

Power Supply

Oscillator

CMUT

Resonator: Surface-functionalized CMUTOscillator: Sustain oscillation. Discrete components or integrated circuitsPower Supply:

A bias voltage (~10-20V) is needed for higher electromechanical coupling but draws no current.Low-voltage supply for the oscillator and the logic

Main processor: PC or MCU with BT wireless

Year 2: We demonstrated VOC gas sensing in 10 ppb resolution with a single functionalized CMUT channel

134

Calibration vapor generator (Model OVG-4, Owlstone Inc., Norwalk, CT) togenerate National Institute of Standards and Technology (NIST) standard traceconcentration level of VOCs.

The functionalized CMUT enclosed in a small acrylic glass chamber (3.5 cm3).

Clean air generated by zero air generator (Model ZAG–6, BCAS Limited, Wallingford Oxon, UK).

Target analyte:Toluene

The CMUT is functionalized with

polyisobutylene(PIB)

Year 2: An average sensitivity of 270 Hz/ppm within the range of 10–20 ppm of toluene

135

CMUT

CC

R2

RE

R1

C2

C1

VSS

VSS

CC

Vout

Rm

Lm

Cm

Rp

C0

Cp

VDC

RB

Schematic of the Colpitts oscillator

The oscillator tracks the change in resonant frequency.

Chemical test results showing frequency shift in response to toluene

The frequency of the oscillator was recorded for 3minutes. Toluene was flowed between 1 to 2minutes.

0 0.5 1 1.5 2 2.5 3-4

-3

-2

-1

0

1

2

3

4

Time (mins)

Freq

uenc

y sh

ift (k

Hz)

10 ppm12 ppm14 ppm16 ppm18 ppm20 ppm

clean air toluene clean air

A 4.52-MHz CMUT is employed as the frequency selective device.

M. M. Mahmud, J. Li, J. E. Lunsford, X. Zhang, F. Y. Yamaner, H. T. Nagle, and Ö. Oralkan, “A Low-Power Gas Sensor for Environmental Monitoring Using a Capacitive Micromachined Ultrasonic Transducer”, IEEE Sensors Proc., 2014, pp. 677-680.

Year 3: We extended our work from a single channel to multi-channel CMUT arrays to demonstrate selectivity

136

CMUT arrays fabricated using our novel 3-mask fabrication process based on anodic bonding.

Three CMUTs: Non-functionalized, Polyvinyl Alcohol(PVA) and Polyisobutylene(PIB). Colpitts oscillators and frequency counters.y ( ) p q y

The test result shows that the PIB coated channel has higher selectivity to toluene.

M. M. Mahmud, M. Kumar, X. Zhang, F. Y. Yamaner, H. T. Nagle, and Ö. Oralkan, “A capacitive micromachined ultrasonic transducer (CMUT) array as a low-power multi-channel volatile organic compound (VOC) sensor,” presented at IEEE Sensors, Busan, Korea (Nov. 1-4, 2015).

Year 3 - 4: Custom low-power frontend integrated circuits

137

M. Kumar, C. Seok, M. M. Mahmud, X. Zhang, and Ö. Oralkan, “A low-power integrated circuit for interfacing capacitive micromachined ultrasonic transducer (CMUT) based gas sensor,” presented at IEEE Sensors, Busan, Korea (Nov. 1-4, 2015).

10 μW with 1-s measurement time every minute.

Oscillator + DFC IC

CMUT

Power Supply

PC

0.18-μm IBM BiCMOS (1.8 mm by 1.8 mm)

Complete system implementation with a digital output.Oscillator + Frequency-to-digital converter

Frequency change recorded digitally by the IC.

Year 4 : Improvement of the frontend IC and complete system integration

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Requirement for the 2nd generation oscillator ICUse the proven IPs from the 1st generation IC.Multi-channel input on-chip multiplexer to handle multiple sensor outputs.Standard digital interface to microcontroller Serial Peripheral Interface(SPI, 4-wire interface.)

Power management unit(PMU)Generates programmable high-voltage CMUT bias(10 – 20 V) and low voltage IC bias (1.8 V) from a single battery source.

Wireless SensingA Bluetooth module with a microcontroller.

Multi-channel CMUT sensor

Year 4 - 5: The second generation front-end integrated circuit with multichannel inputs and SPI

139

Eight analog-input pins with a multiplexer.The Serial Peripheral Interface (SPI)

• A standard way of communication with a microcontroller.

Gate Time

8

Fabricated in a 0.18- m BiCMOS process.

10- W power consumption with a 1.8-V core supply with a duty cycle of 1:60.The lowest modified Allan deviation is 0.95 Hz (1-of 250 ms.

Year 4 - 5: A battery-operated wireless gas sensing prototype + data acquisition system

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Coin-cell battery powered with a power management unit (PMU).Bluetooth low energy (BLE) enabled microcontroller (RFduino).A multichannel CMUT resonant gas sensor + a front-end IC.Wireless real-time multichannel data acquisition and display.

CMUT sensor IC PMUBLE + Coin-cell battery

15cm

4cm

1cm Control S/W: PythonBLE S/W stack: BlueZ

C. Seok, M. M. Mahmud, O. Adelegan, X. Zhang, and Ö. Oralkan, “A battery-operated wireless multichannel gas sensor system based on a capacitive micromachined ultrasonic transducer (CMUT) array,” presented at the IEEE Sensors Conf., Orlando, FL, 2016.

BLE+MCU

CMUT array

PMU + front-end ICIC

Coin-cell battery

1 cm

• Includes a CMUT array, a front-end IC and a PMU only.• Only eight pins need to be connected to the HET.

o SPI (3), 3.3-V supply (1), GND (1), Slave_select (2) and Gate_time (1).

Year 5: The battery-operated wireless gas sensing prototype + data acquisition system is miniaturized

Year 5: Full HET integration will be completed soon

Ongoing/future work

Development of repeatable polymer surface coatings.Novel device structures for CMUT resonators.Demonstration of improved selectivity by using multiple channels with orthogonal functionalization.Further testing in EPA facilities.Extending the system/approach for biosensing.

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HET Featured Project: Ultra Low-Power Sensors for Human Breath and Environmental Monitoring Dr. Veena Misra (North Carolina State University)

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Design and Fabrication of Ultra low-power Gas Sensors

PI: Veena Misra and Bongmook LeeStudents: Michael Lim (Ph. D), Steven Mills (Ph. D) and Akhilesh Tanneeru (Ph. D)

Department of Electrical and Computer Engineering, North Carolina State University

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Goals & MilestonesOverall goal of the project

To develop an ultra low-power,reliable and reusable exhale NO and VOCs in breath sensor as well as environmental gas sensors for use in the ASSIST Health and Environmental Tracker (HET) testbed platforms

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Specific Goals in Year 5

ALD Ozone Sensor in HET Testbed

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6 nm SnO2 sensor testing in EPA chamber revealed increased adsorption/desorption ratio as humidity or temperature increased

Recovery failed at relative humidity of 40%

HET System Testing in EPA chamber

60 ppb40 ppb

100 ppb120 ppb

100 ppb

60 ppb

Ozone levels: 40, 60, 100 and 120 ppb

Humidity levels: 0 and 40%

ALD Ozone Sensor in HET Testbed

148

Increasing baseline resistance restored sensor function for 40% RH at 24 CIncreasing both temperature (32 C) and humidity (40%) again causes recovery failureDynamic tuning of baseline resistance will enable sensor functionality for full range of operating conditions

HET System Testing in EPA chamber

60 ppb 60 ppb

100 ppb120 ppb

100 ppb 100 ppb120 ppb

Ozone levels: 60, 100 and 120 ppb

Humidity levels: 0 and 40%

ALD SnO2 Ozone Sensor

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SnO2 films 6, 12 & 36 nm50 cycles (6-7nm) of SnO2shows much stronger response than thicker filmsCalculated Debye length is on the order of 5 nm so this film is almost completely depleted of carriers causing strong response to charge transfer at surface

Thickness Effect on Sensor Performance

ALD SnO2 Ozone Sensor

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Sensor response increases as deposition temperature decreasesFilms deposited below 150 C are too highly resistive to measure easilyCurrently investigating possibility of impurities in film impacting oxygen vacancy concentration

ALD Deposition Temperature Effect

6nm ALD SnO2

ALD SnxTiyOz Ozone Sensor

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Effect of Composite Sn-Ti metal oxide

Normalized dR/dt higher than pure SnO2 filmsMay be due to increased Schottky barrier heights at grain interfaces or increased oxygen vacancy concentrationsCurrently experimenting with atomic ratios and deposition temperatures

~12 nm ALD Sn0.95Ti0.5O2 films deposited at 200 oC

600 C PDA

ALD SnO2 Acetone Sensor

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Response to acetone vapor at room temperature

0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

200ppm150ppm100ppm50ppm

R/R

%

Time(secs)

7nm of SnO2

14nm of SnO2

10ppm

RH= 90-100%

Thinner SnO2 sensor shows better response at lower concentrationsOperating power is less than 1 microwatt. (7nm 30nW, 14nm

200nW)

ALD SnO2 Acetone Sensor

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0 50 100 150 200 250 300-6

-5

-4

-3

-2

-1

0

1

2

R/R

%

Time(secs)

100ppm Acetone( with >90% RH) Humidity

Humid acetone is 1.5 times more responsive than pure humidity

Demonstrates relevance of our sensor in real-time breath monitoring, as normal human breath contains more than 90% RH.

Evaluating Sensing Mechanism

154

Background:1. Isotherms are used to determine adsorption rate (Keq)

2. Keq is sensing material and gas dependent

Application to ASSIST:• Selection of highest Keq for gases of interest and

lowest Keq for cross-sensitive gases• Enables highly selective/sensitive sensor arrays

Experimental Method:• 6 MHz QCM resonator (bare QCM – Au

electrode only)used to measure mass• Shifts in resonant frequency adsorbed

mass ( )

Standard 6MHz deposition monitor crystal used in adsorption study

R2 = 0.967fmax = -7.397858 Hz

Keq = 1.721517 ppm-1R2 = 0.769, n = 5.631K = 1.793414e-07

Freundlich Fit Parameters Langmuir Fit Parameters=X=mass of adsorbatem=mass of adsorbentn=adsorption intensityK=Freundlich Constant

= + 1fmax=max QCM frequency shift

Keq=equilibrium adsorption rate

Experimental data (circle) can be fitted with isotherms

Milestones

Room Temperature operated acetone sensor (1st quarter, 11/2016) Field testing of ALD metal oxide ozone sensor (1st quarter, 11/2016) VOC sensor stability and reproducibility (3rd quarter, 05/2017) – in progressAdsorption/desorption characteristics of ALD sensor using QCM (2nd

quarter, 02/2016) – in progressComposite metal oxide sensors (4th quarter, 08/2017) – in progress

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Q1 Q2 Q3 Q4Task 1. Design and Fabriate ultra low power VOC sensors

Task 1.1 Fabricate RT acetone sensor

Task 2.1 HET Testbed Field Test at EPA Facility

Task 1.2 Stability and Reproduciblity evaluationTask 2. Ozone sensor optimization and composite metal oxide sensors

Task 2.2 Composite Sensor fabricationTask 3. Study Metal Oxide Sensing Mechanism

Backup

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Task 3: Evaluating Sensing Mechanism

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x

c

c0

xm

c/x

c

slope= 1c = 1K + cLangmuir Isotherm

c=concentration of gas= maximum frequency shift

(saturated surface)Keq=equilibrium reaction rate

Background:1. Isotherms are used to determine adsorption rate (Keq)

2. Keq is sensing material and gas dependentApplication to ASSIST:• Selection of highest Keq for gases of

interest and lowest Keq for cross-sensitive gases

• Enables highly selective/sensitive sensor arrays

Experimental Method:• 6 MHz QCM resonator used to

measure mass• Shifts in resonant frequency

adsorbed mass ( )

Standard 6MHz deposition monitor crystal used in adsorption study

Task 3: Evaluating Sensing Mechanism

158

Expose QCM to 1-5ppm O3 (1ppm steps) fit Langmuir Isotherm

MATLAB fit of for 2-5ppm

y=0.6192*x+ 0.0801R-square: 0.9588vs C

Concentration ( )

(Hz)

= 1K = 1 = 1.614 HzK = 7.753

C vs C

C/(/Hz)

Concentration ( )

is the aK is the rateCalculation of Adsorption rate (O3 to Au)

Task 3: Evaluating Sensing Mechanism

159

Calculation of Adsorption rate (O3 to Au)Expose QCM to 0.5-3ppm NO2(0.5ppm steps) fit Langmuir Isotherm

MATLAB fit of for 1.5-3ppm

y=0.1545*x+0.853R-square: 0.9787

= 1K = 1 = 6.472 HzK = 0.1811is the aK is the rate

Concentration ( )

(Hz)

vs C C vs C

Concentration ( )C/(/Hz)

Lunch

160