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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
5
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)
6
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)
8
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
9
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
12
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
13
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
14
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
15
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
16
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
20
Susan Trolier-McKinstry
Ben CalhounSuman Datta
Omer OralkanJess Jur
Vijay Narayanan
Sustainability Roundtable: Friday @ 10:30am
21
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
24
Student Research Pitches: Thursday @ 2:15pm
25
- 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
26
ASSIST Industry Members
27
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
28
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
31
“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)
32
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
33
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?
34
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?
35
Thank you
36
ASSIST-ing Industry: Engagement and Support
Casey Boutwell, Ph.D., MBA
Director of Industry Engagement
[email protected] (919) 515-3083
37
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
38
Industry LiaisonCasey Boutwell, Ph.D., [email protected](919) 515-3083
ASSIST Industry Members
39
Affiliate Members
Associate Members
Full Members
Coming Soon:
ASSIST Supports Industry
40
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
41
Industry Engages ASSIST
42
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
44
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
45
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
46
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
47
Questions at break please
Director of Industry EngagementCasey Boutwell, Ph.D., [email protected](919) 515-3083
Testbed: Self-Powered and Adaptive Platform (SAP) OverviewJohn Lach – SAP Testbed LeaderUniversity of Virginia
48
3-Plane Diagram
49
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.
50
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
53
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
54
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
55
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
56
SAP Roadmap
57
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)
58
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
80
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
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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
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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
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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
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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
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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
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