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Integrated Circuits and Systems for Biomedical Applications Joonsung Bae [email protected]

Integrated Circuits and Systems for Biomedical Applicationsysmoon/courses/2017_1/grad/12.pdf · 2017-05-30 · Integrated Circuits and Systems for Biomedical Applications ... GSR,

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  • Integrated Circuits and Systems for Biomedical Applications

    Joonsung Bae

    [email protected]

  • Agenda

    Introduction to IMEC

    Wearable Healthcare

    Introduction

    Analog Sensor Interface

    Digital Signal Processing

    Implantable Neural Interface

    Introduction

    ASIC Architecture

    Recording / Stimulating Circuits

    Spike Sorting

    Joonsung Bae 2

  • IMEC – Leuven, Belgium

    Joonsung Bae 3

  • CMOS and beyond CMOS

    Joonsung Bae 4

    Industry’s partner ecosystem

    R&D 300mm cleanroom

    Next-generation logic/memory devices

    3D system integration

    Advanced patterning

  • Wearable

    Chronic disease management

    Lifestyle and preventive care

    Bio-medical sensor SoC

    Wearable prototypeshadware/firmware/sotware/mechanics

    Joonsung Bae 5

  • Life Sciences

    Personalized medicine

    Neuro-Electronics

    DNA analysis

    Single-molecule interaction

    Joonsung Bae 6

  • Data Science and Data Security

    Data extraction

    Data mining

    Data visualization

    Machine learning

    Data security

    Joonsung Bae 7

  • Wearable Healthcare

  • Market Trends

    Joonsung Bae 9

    1990s:

    • PCs

    2000s:

    • Notebooks

    2010s:

    • Smartphones/Tablets

    2020s:

    • Wearable devices

    From portable devices to wearable devices

  • Growing Industry Interest

    Joonsung Bae 10

  • Technology Enablers for Intelligence

    Joonsung Bae

    Cloud computing, Big data analysis

    Flexible electronics

    Autonomous wireless connectivity

    Advanced sensor platform

    Software

    Material

    Protocol

    Hardware

  • Smart sensor

    SensorPhysical property

    Quantitative indication

    Sensing element

    Signal conditioning

    Generate response

    Signal processing

    Electrode

    Antenna

    Transducer

    Microphone

    Photo-detector

    Wireless data transmission

    Actuator response

    User feedback

    Compression

    Feature extraction

    Signal analysis

    security

    Analog interface circuitry

    Analog to Digital Conversion

    12Joonsung Bae

  • Sensor Hardware : SIMBAND

    Joonsung Bae 13

    Tizen OS

    Sensors

  • Cloud Sensor Platform : SAMI

    Like Apple HealthKit Platform

    Open API to analyze the sensed signal

    Continuous and Non-invasive

    Joonsung Bae 14IT대학 교수 세미나

  • Challenges

    Joonsung Bae 15

    Long Term Battery Autonomy

    High Quality and Reliable Data

    Low Cost

    Desirable form factor

    Personalized

    Many sensors

  • Smart Sensor Devices

    Joonsung Bae 16

    Sensor interfaces

    Local processing

    Data collection

    Comm. interface

    Radio

    Storage

    Controller

    Power management

    data stream

    Man

    y se

    nso

    rs

    µProcessorDSP

    Memory

    Accelerators

    DMA

    Local processing Cloud processing

    Clocking

  • Typical Architecture

    Joonsung Bae 17

    AHB

    Processor

    ARM, ARC

    Memory

    SRAM, Flash

    Accelerators

    Filters

    Sample Rate Conversion

    Encryption

    Compression

    Matrix/Vector calculation

    FFT

    Timestamp

    DMA

    ABP

    Sensor

    read-out

    ECG, BioZ,

    GSR, PPG, GP

    Sensor drivers

    LED

    CS (BioZ)

    Oscillator

    CMUPMU

    LDO, DC-DC

    Interfaces

    SPI, I2C, UART, GPIOTimers

    RTC

    Analog Digital

  • Analog Sensor Interface

  • Signal Characteristics

    19

    [Webster1992]

    10mV

    1mV

    100uV

    10uV

    1uV

    100nV

    1Hz 10Hz 100Hz 1kHz 10kHz

    ECG

    Action Potentials

    LFP

    ECoG

    EEG

    EMG EMG

    ECG

    EEG

    ECoG&

    LFPAP

    Joonsung Bae

  • Sensing Challenges

    Joonsung Bae 20

    Cbo

    dy-t

    o-

    main

    s

    BIASVBZbias

    Zelec2

    Zelec1

    V2

    V1

    VB

    MAINS

    Sensor

    Interface

    DC-offset:

    Mains interference (VCM):

    Electrode Impedance Mismatch converts VCMinto VDM

    21 VVVDC

    BIASCCM ZIV

    V V CMZIN

    Z ZIN E2

    Inherent CMRR

    V V CMZIN

    Z ZIN E1

    DV

    VCM=

    DZ

    ZIN

  • General Implementation

    Joonsung Bae 21

    Filters PGAIA

    AA

    ADC Filters

    sensor

    signal Digital samples

    #[email protected] rateOther phases / frequencies

    DC Control

    Application requirements

    High input impedance

    Low thermal noise

    Low 1/f noise

    Large dynamic range

    Low power

    High CMRR

    Low offset

    Design techniques

    Chopper modulation

    Correlated double-sampling

    DC-servo

    Bootstrapping

    Design in weak inversion

  • Digital Signal Processing

  • Accelerators - Motivation

    Joonsung Bae 23

    A processor (like e.g., ARM Cortex M0) is popular for embedded applications

    Ease of programming

    Availability of tools and hardware

    However:

    optimized for controlling tasks.

    DSP tasks (like data filters) not well supported needs many more cycles

    Latency due to software (Real-time issue)

    Might be less accurate

  • Accelerators

    Joonsung Bae 24

    For sensor modules several accelerators might make sense:

    Digital filtering (LPF, HPF, CIC)

    Sample Rate Conversion

    Motion Artefact Reduction

    Time-stamping

    Encryption

    Compression

    Vector/Matrix calculations

    FFT

    ....

    They off-load the processor and can do the calculations faster, more accurate and with less energy

  • Motion Induced Signal Artifacts

    Joonsung Bae 25

    Ambulatory ECG monitoring

    Enabling continuous health monitoring under user’s daily routine

    Body movement occurs significant Motion Artifacts on ECG signal, which suffers from ..

    Poor signal quality

    Potentially wrong clinical diagnosis

    Steady Motion induced

  • PCA Processing for Artifact Reduction

    Condense Measured data set into a few “principal components”

    Principal components are a linear combination of the data set, with weights chosen so that they are mutually uncorrelated

    Joonsung Bae F. Castellas et. al. “Principal Component Analysis in ECG Signal Processing”

    8 channelsMeasured ECG Processed ECG

    4 principalcomponents

    Mutuallyuncorrelated

    PCAProcessing

    PCA is powerful algorithm for MA reduction Because MA and ECG are uncorrelated

  • PCA Algorithm

    Measure M-channel ECGs with N-samples

    Define the data matrix X

    Calculate the covariance matrix Sx

    Calculate the Eigenvectors and Eigenvalues of the Sx

    Chose feature vector (P) with eigenvectors

    Final Data = P ∙ X

    Joonsung Bae F. Castellas et. al. “Principal Component Analysis in ECG Signal Processing”

    Mch

    an

    nels

    N samples

    X∙XT (multiply a large matrix with its transposed matrix)P∙X (multiply a small matrix with a large matrix)X-E (column-wise subtraction for eigenvalue calculation)

    Huge matrix operations

    H/W accelerator for vector / matrix operations is needed to

    reduce the burden on M0

  • Vector / Matrix Operations

    Principal Component Analysis (PCA) and Independent Component Analysis (ICA) to separate any noise from ECG signal by making use of N-channel ECG signal

    Joonsung Bae 28

    ▸ Transposed

    multiplication

    ▸ Matrix

    multiplication

    ▸ Matrix row

    summation

    Rnn = Anm . AT

    mn

    n = 1..4, m = 1 ... 5120

    Rm =

    Processor

    Memory MAU accelerator

    AHB

  • Matrix Processor Kernel

    Joonsung Bae 29

    Matrix to Matrix multiplication

    Accumulator in the inner loop

    Define ResultR[COUNT_2][COUNT_1];

    For INDEX_1=0 to COUNT_1 -1 { // for each col (B)

    For INDEX_2=0 to COUNT_2 -1 { // for each row (A)

    Rval=0;

    For INDEX_3=0 to COUNT_3 -1 {

    Rval = Rval + (operandA[INDEX_2][INDEX_3] *

    operandB[INDEX_3][INDEX_1]);

    } // for each col(A) & row(B)

    ResultR[INDEX_2][INDEX_1] = Rval

    }

    }

    Pseudo code

    CO

    UN

    T_

    2

    COUNT_3 COUNT_1

    CO

    UN

    T_

    3

    CO

    UN

    T_

    2

    COUNT_1

  • Benefits

    Joonsung Bae 30

    Perform X*XT

    Software realization running on the Cortex M0 vs. Matrix Processor

  • ECG with Motion Artifact Reduction

    Joonsung Bae

    PCA processing with concurrent 3-lead ECG signal

    3-channel ECG (512 S/s)

  • SoC Implementation

    Joonsung Bae 32

    0.18mm CMOS

    7.2 mm x 8mm

    10.4MHz core clock

    On-chip 192kB SRAM

    Continuous data collection with analog and digital

  • Future Direction

    ISSCC 2017

    Joonsung Bae 33

  • EEG Artifact Reduction in Analog

    Joonsung Bae 34

  • Analog Signal Processing

    Joonsung Bae 35

  • Linear Transforms

    Joonsung Bae 36

  • Dot Product Unit

    Joonsung Bae 37

  • Fully Autonomous System

    Acoustic Sensing and Object Recognition System

    Joonsung Bae 38

  • Future Directions

    Joonsung Bae 39

    Machine learning!! Context-aware sensing, Motion artifact, Personalization

    Dedicated Hardware with M0 or

    Software Implementation with upgraded core like M4

  • Implantable Neural Interface

  • Central Nervous System

    Joonsung Bae 41

  • Neural Interface – Closed Loop

    Joonsung Bae 42

  • Peripheral Nervous System

    Joonsung Bae 43

    Sensory perception

    Motor functionality

  • CMOS Chip in the Nerve

    Joonsung Bae 44IT대학 교수 세미나

    x8 Readout Channels

    x8 Readout Channels

    x16 Stimulation Units

    x32 Recording Pixels

    Switch Matrix

    x32 Recording Pixels

    Dig

    ita

    l U

    nit

    500 μm

    Time [ms]

    Delicate surgery procedure

    MultidisciplinaryCooperation

    Chip designer

    Processing engineer

    Neural scientist

    Neural surgeon

  • Where to sense in Vivo

    Joonsung Bae 45

    45

    Electrode

    Skull

    Stratum Corneum

    SubcutaneousLayers

    Brain

    Electrode

    Electrode

    Skin

    EEGSubcutaneous

    Scalp

    EEG ECoG LFPAction

    Potentials

    Non-Invasive Invasive

    Macroscopic

    Microscopic

  • ASIC Architectures

  • System Architecture

    Joonsung Bae 47

    Ele

    ctr

    odes

    Analog amplification

    & filtering

    A/D Signal processing

    Data transmission

    Data

    analy

    sis

    Stimulation circuitry

    D/A Digital control

    Data transmission

    How to partition the system

  • Signal Processing and Integration

    Joonsung Bae 48

    What to do with all

    that data??

    Local signal processing

    Data reduction/compaction

    Feature extraction

    Reduced output data rate and consequently reduces TX power

    But, algorithms can be easily computationally-intensive

    Need trade-off between TX power vs processing power

    Algorithm and hardware optimization is required

    Transmission Data

    Processing Power

  • Passive Arrays

    Joonsung Bae 49

    Integrated External

    Ele

    ctr

    odes Analog

    ampl.A/D Signal

    processingData

    transmission

    Data

    analy

    sis

    Stimulation circuitry

    D/A Digital control

    Data transmission

    [Campbell et. al. 1991]

  • Active Arrays

    Joonsung Bae 50

    [Lopez et. al. 2013, Lopez et. al. 2016, Raducanu et. al. 2016]

    Integrated External

    Ele

    ctr

    odes Analog

    ampl.A/D Signal

    processingData

    transmission

    Data

    analy

    sis

    Stimulation circuitry

    D/A Digital control

    Data transmission

  • Active Arrays

    Joonsung Bae 51

    Target: reduce output data rates and power

    Spike detection => only transmit spike data and timestamps

    Spike sorting => only transmit cluster information

    Lossless data compression

    Other advantages:

    Simple close-loop functionality

    Increased number of readout channels (> 1000)

    Integrated External

    Ele

    ctr

    odes Analog

    ampl.A/D Signal

    processingData

    transmission

    Data

    analy

    sis

    Stimulation circuitry

    D/A Digital control

    Data transmission

  • Wireless Active Probes

    Joonsung Bae 52

    Advantages:

    Autonomous system => no cables

    Reduced packaging effort

    Reduced tissue damaged => untethered probe

    Versatile freely-moving animal experiments

    Challenges:

    Power required for wireless transmission

    Bandwidth

    Integrated External

    Ele

    ctr

    odes Analog

    ampl.A/D Signal

    processingData

    transmission

    Data

    analy

    sis

    Stimulation circuitry

    D/A Digital control

    Data transmission

  • Fully Autonomous Active Probes

    Joonsung Bae 53

    Dream solution:

    On-chip data analysis for advanced close-loop applications

    Minimum data transmission: only configuration or status

    Challenges:

    Design of machine learning algorithms

    Minimize size of integrated system

    Integrated

    Ele

    ctr

    odes Analog

    ampl.A/D Signal

    processing

    Data

    analy

    sis

    Stimulation circuitry

    D/A Digital control

  • Recording / Stimulating Circuits

  • Neural Recording Circuits

    Joonsung Bae 55

    How to design a good neural amplifier

    Recording

    Electrode

    Reference

    Electrode

    Low-Noise

    Neural

    Amplifier Filter

    Programmable

    Gain Amplifier ADC

  • Neural Amplifier Specs

    Joonsung Bae 56

  • Neuro-modulation

    Joonsung Bae 57

    Non-invasive

    E.g. tENS (Transcranial Electrical Nerve Stim.)

    E.g tMS (Transcranial Magnetic Stimulation)

    Invasive

    E.g cochlear implants

    E.g. Deep Brain Stimulation

    Various stimulation methods

    Electrical

    Optical

    Chemical

    Ultrasound

  • Electrical Stimulation

    Joonsung Bae 58

  • Electrical Stimulation

    Joonsung Bae 59

    IStim

    Tissue

    WE

    CEIStim

    IStimIStim

    S1

    S1

    S2

    S2

    S3 S3

    VSS

    VDD VDD

    VSS(b)

    ON OFFS1

    S2

    S3

    IStim

    Tissue

    WE CE

    IStim

    IStimIStim

    S1

    S1

    S2

    S2

    VSS

    VDD VDD

    VSS(a)

    ON OFFS1

    S2

    Passive charge balancing

    Series capacitor to block DC current into the tissue (avoid charge buildup)

    Short to ground (usually limits stimulation frequency)

  • Spike Detection & Sorting

  • Neural Probe Data Bottleneck

    Joonsung Bae 61

    Upscaling will generate ever more data.

    Example:

    Sampling Rate = 30kSps

    Resolution = 10bits

    Number of Channels = 1000

    Total Data Rate = 300Mbps

    What to do with all that data??

  • Neural Systems

    Joonsung Bae 62

    Transmitting raw data over wireless is too power hungry!

    Mostly wired solutions are used today

    Bulky and unreliable

    Impose serious limits on the experiments that can be performed

    7.2 mm

    20 mm

    3 mm

  • Spike detection

    63

    Raw signal

    Filtering

    Emphasis

    Threshold

    Window

    Goal:

    Find the regions in the raw signal

    where a spike is present and

    discard the rest.

    Joonsung Bae

  • Spike detection

    64

    Raw signal

    Filtering

    Emphasis

    Threshold

    Window

    BPF removes undesired signals:

    baseline wander

    LFP

    High-frequency noise

    [S. Gibson 2012]

    Joonsung Bae

  • Spike detection

    65

    Raw signal

    Filtering

    Emphasis

    Threshold

    Window

    Filtered data

    NEO

    Amplify spike for more reliable thresholding

    Avoid erroneous spike detection

    Several methods

    Non-linear energy operator

    Template matching

    [S. Gibson 2012]

    Joonsung Bae

  • Spike detection

    66

    Raw signal

    Filtering

    Emphasis

    Threshold

    Window

    Retain only spike signals

    Significant data reduction

    NEO

    [S. Gibson 2012]

    Joonsung Bae

  • Spike alignment

    Threshold crossings are typically noisy

    Need to find a better way to properly align the detected spikes for efficient feature extraction and spike clustering.

    Align based on local maximum

    Align based on maxim slope

    67

    After spike detection After spike alignment

    [S. Gibson 2012]

    Joonsung Bae

  • Spike Sorting

    Joonsung Bae 68

    Recorded signal = superposition of signals originating from various neurons close to the recording site.

    Which neuron fires when?

    Different neurons generate different spike shapes

  • Spike Sorting

    Joonsung Bae 69

    Spike sorting:

    identify spike times of individual neurons

    “which neuron fires when”

    Obviously reduces data rate significantly

    But is also often the first step required for proper data interpretation

    Neuron A

    Neuron B

    Neuron C

    Spike sorting

  • Feature extraction

    Map time-domain waveform onto multi-dimensional feature space

    Simple example:

    Peak-to-peak amplitude

    Spike width

    More complex algorithms extract much more features:

    Principle Component Analysis (PCA)

    Discrete Wavelet Transform (DWT)

    70

    AmplitudeW

    idth

    Joonsung Bae

  • Clustering

    Identify clusters in the multi-dimensional feature space

    Clusters indicate similar spikes spikes originating from the

    same neuron

    Very active field of ongoing research (cfr. machine learning)

    71

    NEURON A spikes

    NEURON B spikes

    NEURON C spikes

    Joonsung Bae

  • Neural Signal Processing

    Joonsung Bae 72

    [Gibson 2012]

  • Future Directions

    Joonsung Bae 73

    Incredible progress, yet still such a long way to go

    High-density analog

    Digital signal processing

    Material science & processing technology

    Wireless power and data

    Algorithm for efficient spike

    sorting

    Protocol for autonomous connectivity

  • Any Suggestions?

    배준성 (裵俊盛)

    [email protected]

    http://sites.google.com/view/kwbics

    mailto:[email protected]://sites.google.com/view/kwbics