flexible and stretchable organic artificial nerves...bio-inspired electronics and robotics...

Flexible and Stretchable

Organic Artificial Nerves

Tae-Woo Lee (李泰雨)

Dept. of Materials Science and Engineering

Seoul National University

(e-mail: [email protected])

The 16th U.S.-Korea Forum on Nanotechnology

2019.09.24

2

Contents

Y. Kim+, A. Chortos+, W. Xu+*, Z. Bao*, T.-W. Lee* et al, Science, 360, 998 (2018)

Y. Lee+, J.Y. Oh+, Z. Bao*, T.-W. Lee* et al, Science Advances, 4, eaat7387 (2018)

W. Xu, T.-W. Lee* et al, Science Advances, 2, e1501326 (2016)

3

Bio-inspired systems for engineering devices

Biological systems can inspire new generations of engineering devices.

In our body, sensory, neural, and motor processing tasks are done

extremely efficiently and robustly with extremely low energy consumption,

in very little volumes

The entire brain and body are put together with energy-efficient neurons

and cells to robustly perform complex information-processing tasks.

One can learn a lot of things from biology to develop efficient

technologies, to learn to architect systems that can perform efficiently

and reliably with unreliable devices, to build systems that automatically

learn and adapt to a changing environment.

Biological

mechanoreceptor

(i) Pressure

Nerve fiber

(iv) Postsynapticpotential

(ii) Receptor potential change

Biological synapse

(iii) Action potentials

4

Humanoid robots

- Shape like human

- Move like human

- Sense like human

- Think like human

Bio-inspired electronics & soft robotics

5

Bio-inspired soft electronics and robots

Artificial Muscle

Motor system

Electronic Skin & Sensors Neuromorphic

Artificial NervesCommunications of the ACM, 2012, 55, 76-87

Bio-inspired electronics and robotics moves/senses/thinks like a human

6

Our Research Direction in Flexible Electronics (Tae-Woo Lee’s Group)

Neuro-inspired Organic Artificial Sensory Nerves

7

Organic Nanowire Synapses

ONW synaptic transistor that emulates a biological synapse not only in morphology, but also in important working principles.

biological spikes artificial spikes

A A’

BB’

biological EPSC artificial EPSC

conducting line

ion gel

core-sheath ONW

Axon

Dedron

• The conductive lines and probe (A’) mimic an axon (A) that deliver presynaptic spikes from a preneuron to the presynaptic membrane.

• An ONW (B’) mimics a biological dendron (B) in which an EPSC is generated in response to presynaptic spikes and is delivered to a postneuron.

Synaptic cleft

ProbePresynapticMembrane

Large-scale alignment and integration of 1D materials is

still a difficult challenge for large-scale circuit applications

8

ONW Synaptic transistors (Short term potentiation)

60 70 80 900.0

2.0n

4.0n

6.0n

8.0n

Post-

synaptic c

urr

ent (A

)

Time (s)

EPSC

W. Xu, T.-W. Lee* et al, SCIENCE Adv. 2, e1501326 (2016)

Random Accumulation Return to random

Accumulated Anions

attracts a similar

number of holes in the

P3HT channel

STP

9

Schematic of the working mechanism of ONW ST for long-term plasticity

The spontaneous release of the trapped anions in the ONW is slow, inducing long-term memory.

LTP

10

ONW Synaptic transistors

Long-term plasticity

- Long-term potentiation (LTP) that usually occurs at excitatory synapses, which is a persistent increase in synaptic strength following a number of consecutive stimulations of a synapse.

- Consecutive 30 negative pulses accumulates and increased EPSC- Long-term retention obtained W. Xu, T.-W. Lee* et al, SCIENCE Advances, 2, e1501326 (2016)

11

Energy consumption per synaptic event of current available synaptic devices

2008 2010 2012 2014 20161a

1f

1p

1n

1μ

ONW ST

NG ONW ST

PCM

RRAM

Conductive bridge

Ferroelectric

Thin film ST

E

ne

rgy c

on

su

mp

tio

n (

J)

Year

BIOLOGICAL REGION

~1.23 fJ per synaptic event for individual ONWwas successfully attained, which can even rival that of the biological synapses.

W. Xu, T.-W. Lee* et al, SCIENCE Advances, 2, e1501326 (2016)

12

Contents




13

Artificial Central Nervous System – Brain-inspired Computing

Brain-inspired Organic Artificial Synapse

T.-W. Lee et al., Sci. Adv., 2016 Nat. Mater., 2017

Materials for CNS

- Long-term potentiation

- Non-volatile memory property

Redox active polymer

Electrochemical ion doping

mechanism

PEDOT:PSS + PEIP3HT + [EMIM][TFSI]

T.-W. Lee et al., Sci. Adv., 2016 Nat. Mater., 2017

14

ONW Synaptic Transistor to mimic Peripheral nervous System

Lee et al, Sci. Adv. 2016, 2, e1501326 Salleo et al, Nat. Mater. 2017, 16, 414

Neuromorphic

computing & memory

1. Autonomic nerve system

2. Somatic nerve system:- Sensory neuron- Motor neuron

Peripheral nervous system:

15

Artificial afferent and efferent nerves

Artificial nerves

Afferent nerve : axons

of sensory neurons

carrying sensory

information from body

Efferent nerve : axons

of motor neuron

Applications of

artificial nerves:

robotics and

prosthetics with the

combination of

sensors and motors

16

Artificial Mechano-Sensory Nerves

Y. Kim, A. Chortos, W. Xu*, Z. Bao*, T.-W. Lee*, et al, Science, 360, 998 (2018)

https://www.youtube.com/watch?v=IrYTD1xZVSs

17

Biological mechanosensory nerve

Receptor

potential

Pressure

Frequency of

action potential

Pressure

Pressure

Receptor

potential

Frequency of

action potential

• Network of neurons and synapses in brain

processes information

→ Biological mechanosensory system processes pressure information

• Their frequencies deliver information.

18

Biological sensory nerves

Artificial sensory nerves


Artificial mechanosensory nerve

19

Resistive pressure sensor mimicking mechanoreceptor

0.0 30.0k 60.0k 90.0k10k

1M

100M

10G

1T

100T Bias=-1V

Bias=-3V

Bias=-5V

Re

sis

tan

ce

(

)

Pressure (Pa)

Applied pressure

decreases the resistance of

pressure → sensors mainly by reducing contact

resistances.

Biological SA-I mechanoreceptor:pressure input intensity = 1-100 kPa


20




Artificial mechanosensory nerves

21

Ring Oscillator Output

0

-1

-2

4020

Vo

ltag

e (

V)

Time (ms)0

Voltageoutput

VDD = -2.3 V, VLL = -4.6 V, VHH = 1 V Oscillating frequency = 20-89 Hz

Biological mechanosensorynerves:Action potential frequency range= 0.4-100 Hz

3-stage ring oscillator


22

Pressure sensor + ring oscillator

Re

cep

tor

po

ten

tial

Pressure

Biological mechano-receptor

(i) → (ii) (i) → (ii) → (iii)

Fre

qu

en

cy o

fac

tio

n p

ote

nti

alPressure

0 30 60 90

0

-1

-2

-3

-4

-5

Su

pp

ly v

olt

ag

e t

o

rin

g o

scil

lato

r (V

)

Pressure (kPa)

0 30 60 90

0

20

40

60

80

100

Fre

qu

en

cy (

Hz)

Pressure (Pa)

Pressure sensitivity=

2-10 Hz kPa-1

Pressure sensitivity=

0.4-13 Hz kPa-1

Supply voltage

Organic ring oscillator #1(i) Pressures (ii) Supply voltagesto ring

oscillator

(iii) Oscillating voltages

Resistive pressureSensor #1

frequency changeSupply voltage

change

23




Mechanosensory nerves

∙ Vout of ring oscillators connected to the gate of

synaptic transistors

24

Emulation of Signal Transmission in Biology

Naturalistic Sensory and Motor Response

Artificial Peripheral Nervous System

Materials design for PNS

Short-term potentiation

Volatile & Fast decay

Low ion doping efficiency

Electric double layer

DonorAccep

tor

n

• Fast decay – Electrical double layer

• Low ion doping efficiency

Donor-Acceptor polymer

Short Decay time

Peak

Peak×1/e

• Stretchable – Nanowire Transistors

My Approaches

Neuromorphic Bioelectronics

25

Ion-gel transistors (synaptic transistors)

Biological Synapses in the somatosensory system:Decay time of postsynaptic currents= 1.5-5 ms

Decay time

=2.4 ms

Peak

Peak×1/e

Time (ms)

Dra

in c

urr

en

t (

A)

010 200

-1

-2

-3

-4 0

1

2

Gate

vo

ltag

e (V

)

Polymer

semiconductorsPolymer 1 P3HT

Decay time

(ms)2.35 ± 1.02 299 ± 201 s

Polymer 1


Decay times for postsynaptic currents (typically 2 to 3 ms)

Decay time of postsynaptic outputs reasonably short enough to receive pressure inputs at the desired frequency

26

∙ Increase of magnitude (A, B) & duration (C, D) of pressure

→ increase of peak postsynaptic currents

∙ Frequency of postsynaptic currents independent of

duration of pressure stimulus

∙ SA-I receptors (pressure input frequencies are ~200 ms)

Pressure sensor + ring oscillator + synaptic transistors

A B C

D


27

Integration of pressure inputs

30 45 60 75 90 105

|Fo

uri

er

tran

sfo

rm|

Frequency (Hz)

Pressure

sensor#1

Pressure

sensor#2

Ring

oscillator#1

Ring

oscillator#2

Synaptic

transistor

69 Hz 91 Hz

20 kPa

80 kPa

20kPa and 80kPa

Sum of (B) and (C)

Gate

1

Gate

2

Gate

N

So

urc

e

Dra

in

I


Synaptic transistor: an adder

∙ Pressure signals from multiple pressure sensors are combined.∙ Amplitude & frequency was maintained after signal integration in synaptic transistor

20kPa

80kPa80kPa

0

-1

-2

-3

-4

-5

Po

sts

yn

ap

tic c

urr

en

t (

A)

Time (s)

0 0.05 0.1 0.15 0.2

0

-1

-2

-3

-4

-5

Po

sts

yn

ap

tic c

urr

en

t (

A)

Time (s)0 0.05 0.1 0.15 0.2

Sum of (A) and (B)

20 kPa 80 kPa

20 kPa and 80 kPa

0

-1

-2

-3

-4

-5

Po

sts

yn

ap

tic c

urr

en

t (

A)

Time (s)0 0.05 0.1 0.15 0.2

0

-1

-2

-3

-4

-5

Po

sts

yn

ap

tic c

urr

en

t (

A)

Time (s)0.20.150.10.050

(A) (B)

=

(C)

Synapses

Presynaptic

neuron #1

Presynaptic

neuron #2

Postsynaptic

neuron

Biological nervous system

28

Movement recognition and braille reading

Pressure

sensor#1

Ring

oscillator

Synaptic

transistor

Pressure

sensor#2

-4

-3

-2

-1

0

321

Po

sts

yn

ap

tic c

urr

en

t (

A)

Time (s)0 0 1 2 3 4 5

-4

-3

-2

-1

0

Po

sts

yn

ap

tic

cu

rre

nt

(A

)

Time (s)

0

100

200

300

Sm

all

est

Vic

tor-

Pu

rpu

ra d

ista

nc

e

(DV

P)

betw

ee

n a

lph

ab

ets

Pressure sensor

+ring oscillator

Pressure sensor

+ring oscillator

+synaptic transistor

100

75

50

25

0

(Hz)

Braille

“E”

80

40

0

(kPa)

Pressure

sensors

Ring

oscillators

Synaptic

transistors

Movement recognition

Braille reading

Biological mechanosensorynerves: Branched mechano-

receptor structure


A larger DVP means more dissimilarity between two spike trains.

Braille letters became more distinguishable

29

Hybrid reflex arc (artificial afferent nerve)

4 mm

Force

gauge

Reference

electrode

Stimulating

electrode Direction

of force2 mm

Force

Pressure

Postsynapticcurrent

Hybrid monosynaptic reflex arc

Amplifiedvoltage

Connection to efferent nerve

0 20 40 60 80

0

10

20

30

40

50

60

Fo

rce o

f le

g e

xte

nsio

n (

mN

)

Pressure (kPa)

1.00.80.60.40 0.2

50

40

30

20

10

Time (s)Fo

rce

of

leg

ex

ten

sio

n (

mN

)

0

0

20

40

Pre

ss

ure

(kP

a)

leading to the actuation of the tibial extensor muscle in the leg

30Y. Kim, A. Chortos, W. Xu*, Z. Bao*, T.-W. Lee*, et al, Science, 360, 998 (2018)

Hybrid reflex arc (artificial afferent nerve)

31

Contents




32

Optical stimulation

Presynaptic impulses

Postsynaptic response

Neuromuscular junction

Muscle contraction


Optical stimulation


Artificial synapse

Muscle contraction

• Stretchable artificial synapse is

necessary for artificial motor system

of neuro-inspired soft robots with

various motions.

• Motor Neuron

Presynaptic membrane = Gate

electrode

Presynaptic potential = Gate

voltage

• OptogeneticsPhotosensitive protein =

Photodetector

• Neuromuscular junction

Synaptic cleft = Ion-gel electrolyte

Neurotransmitter = Anion

• Skeletal muscle

Postsynaptic membrane = OSC NW

Postsynaptic potential = Drain

current

Muscle fiber = Polymer actuator

Artificial Optoelectronic Neuromuscular System

Human optogenetic sensorimotor nervous system

genetically-

modified motor

neurons

This approach is promising to restore the motor

function of defective neuromuscular systems

Y. Lee+, J. Y. Oh+, Z. Bao*, T.-W.Lee* et al, SCIENCE Adv., 4, eaat7387 (2018)

33

+ + ++

++

+

+

‒ ‒ ‒‒

‒

‒

‒

‒

+ +

+

+

+

+

Transimpedance circuit

Polymer actuator

Photodetector

Synaptic transistor

0 10 20 30 40 50 600

1

2

3

100% strain

Number of spikes

(

mm

)

0

1

2

3

4

Outp

ut voltage (

V)

0% strain 100% strain

100 spikes 100 spikesinitial

Optical Neuromuscular Electronic Synapse

artificial muscle fiber

Y. Lee+, J. Y. Oh+, Z. Bao*, T.-W.Lee* et al, SCIENCE Adv., 4, eaat7387 (2018)

34

Without artificial synapse (Constant displacement with constant voltage)

Our synapse (Contraction of artificial muscle gradually increases as the fixed

light pulses (action potentials) are applied repeatedly)

Optical Neuromuscular Electronic Synapse

• More similar to biological muscle contraction

0 10 20 30 40 50 600

1

2

3

100% strain

Number of spikes

(

mm

)

0

1

2

3

4

Outp

ut voltage (

V)

Nat., Commun., 2013,

Nat., Commun., 2016

35

Contents




36


Optical stimulation


Artificial synapse

Muscle contraction

• Development of a stretchable artificial synapse and a novel bio-inspired sensorimotor system.

• Suggesting a communication method of human/machine interface.

• Promising strategy to advance soft robotics, neuro-inspired robotics and neuroprothetics.

Summary

Soft exosuit prosthetics

Soft robotics

NeuroroboticsForce

Pressure

Postsynapticcurrent

Hybrid reflex arc

Amplifiedvoltage

Connection to efferent nerve

Wearable Neuroprosthetics

37

Thanks for your attentionPrinted, Flexible Nano-Electronics & Energy

Laboratory (PNEL) at SNU

Acknowledgements

Stanford University

• Prof. Zhenan Bao

• Dr. Yeongin Kim

• Dr. Alex Chortos

• Dr. Jin Young Oh

• Mr. Yuxin Liu

POSTECH

• Prof. Hyunsang Hwang

• Prof. Moon Jeong Park

Wentao Xu

Yeongjun Lee

flexible and stretchable organic artificial nerves...bio-inspired electronics and robotics...

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