Flexible Flex ible an and Str d Stret etch chab able le - PowerPoint PPT Presentation

The 16th U he 16th U.S .S.-Kor orea ea For orum um on N on Nanotec anotechnolog hnology 2019.09.24 2019.09.24 Flexible Flex ible an and Str d Stret etch chab able le Organ Org anic ic Ar Artifi tificial cial Ner Nerve ves Tae-Woo Lee ( 李泰雨 ) Dept. of Materials Science and Engineering Seoul National University (e-mail: twlees@snu.ac.kr)

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

Bio Bio-insp inspir ired ed s syste ystems ms for or en engine gineering ering de device vices  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. (i) Pressure (iv) Postsynaptic potential (ii) Receptor potential change (iii) Action potentials Biological mechanoreceptor Nerve fiber Biological synapse 3

Bio Bio-insp inspir ired ed electr electronics onics & & so soft ft robo obotics tics Humanoid robots - Shape like human - Move like human - Sense like human - Think like human 4

Bio Bio-insp inspir ired ed so soft elect ft electron onics ics an and r d rob obot ots  Bio-inspired electronics and robotics moves/senses/thinks like a human Artificial Muscle Electronic Skin & Sensors Neuromorphic Motor system Artificial Nerves 5 Communications of the ACM, 2012 , 55, 76-87

Our Research Direction in Flexible Electronics (Tae- Woo Lee’s Group) Neuro-inspired Organic Artificial Sensory Nerves 6

Organic Nanowire Synapses  ONW synaptic transistor that emulates a biological synapse not only in morphology, but also in important working principles. artificial spikes biological spikes A A’ conducting line Axon Probe Presynaptic Membrane ion gel B’ Synaptic cleft B core- Dedron sheath biological EPSC artificial EPSC ONW Large-scale alignment and integration of 1D materials is still a difficult challenge for large-scale circuit applications • 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. 7

ONW Synaptic transistors (Short term potentiation) Random Accumulation Return to random STP 8.0n Post-synaptic current (A) 6.0n Accumulated Anions attracts a similar number of holes in the 4.0n P 3 HT channel EPSC 2.0n 0.0 60 70 80 90 Time (s) 8 W. Xu, T.-W. Lee* et al, SCIENCE Adv. 2, e1501326 (2016)

Schematic of the working mechanism of ONW ST for long-term plasticity LTP The spontaneous release of the trapped anions in the ONW is slow, inducing long-term memory. 9

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) 10

Energy consumption per synaptic event of current available synaptic devices 1μ ~1.23 fJ per synaptic event for individual ONW was successfully attained, which can even rival that of the biological synapses. Energy consumption (J) 1n 1p ONW ST NG ONW ST BIOLOGICAL REGION PCM 1f RRAM Conductive bridge Ferroelectric Thin film ST 1a 2008 2010 2012 2014 2016 Year W. Xu, T.-W. Lee* et al, SCIENCE Advances, 2, e1501326 (2016) 11

Contents W. Xu, T.-W. Lee* et al, Science Advances, 2, e1501326 (2016) 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) 12

Artificial Central Nervous System – Brain-inspired Computing  Brain-inspired Organic Artificial Synapse  Materials for CNS - Long-term potentiation - Non-volatile memory property T.-W. Lee et al., Sci. Adv., 2016 Nat. Mater., 2017  Redox active polymer  Electrochemical ion doping mechanism PEDOT:PSS + PEI P3HT + [EMIM][TFSI] T.-W. Lee et al., Sci. Adv., 2016 Nat. Mater., 2017 13

ONW ONW Synap Synaptic T tic Tra ransisto nsistor to r to mimi mimic c Periphe Periphera ral n l ner ervou vous s System System Neuromorphic computing & memory Lee et al, Sci. Adv. 2016 , 2, e1501326 Salleo et al, Nat. Mater. 2017 , 16 , 414 Peripheral nervous system: 1. Autonomic nerve system 2. Somatic nerve system: - Sensory neuron - Motor neuron 14

Artificial nerves  Artificial afferent and efferent 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 15

Ar Artificial tificial Mec Mecha hano no-Sen Sensor sory y Ner Nerves es https://www.youtube.com/watch?v=IrYTD1xZVSs Y. Kim  , A. Chortos  , W. Xu  *, Z. Bao*, T.-W. Lee*, et al, Science , 360, 998 (2018) 16

Biologica Biological mec mecha hano nose sens nsor ory nerve nerve • Network of neurons and synapses in brain processes information • Their frequencies deliver information. Pressure Receptor potential Receptor Pressure potential Frequency of Frequency of action potential action potential Pressure → Biological mechanosensory system processes pressure information 17

Ar Artifi tificial cial mec mecha hano nose sens nsor ory ne nerve Biological sensory nerves Artificial sensory nerves Y. Kim  , A. Chortos  , W. Xu  *, Z. Bao*, T.-W. Lee*, et al, Science , 360, 998 (2018) 18

Resistive Resistive pressure pressure sensor sensor mimicking mimicking mechanor mechanorecepto eceptor 100T Bias=-1V 1T Bias=-3V Resistance (  ) Bias=-5V 10G Applied pressure Biological SA-I decreases the resistance of mechanoreceptor: 100M pressure → sensors mainly pressure input by reducing contact 1M intensity = 1-100 kPa resistances. 10k 0.0 30.0k 60.0k 90.0k Y. Kim  , A. Chortos  , W. Xu  *, Z. Bao*, T.-W. Lee*, et al, Science , 360, 998 (2018) 19 Pressure (Pa)

Ar Artifi tificial cial mec mecha hano nose sens nsor ory ne nerves es Biological sensory nerves Artificial sensory nerves Y. Kim  , A. Chortos  , W. Xu  *, Z. Bao*, T.-W. Lee*, et al, Science , 360, 998 (2018) 20

Ring Os Ring Oscillator cillator Outpu Output 3-stage ring oscillator Voltage output V DD = -2.3 V, V LL = -4.6 V, V HH = 1 V Oscillating frequency = 20-89 Hz Biological mechanosensory -2 nerves: Voltage (V) Action potential frequency range= 0.4-100 Hz -1 0 20 40 0 Time (ms) Y. Kim  , A. Chortos  , W. Xu  *, Z. Bao*, T.-W. Lee*, et al, Science , 360, 998 (2018) 21

Pressure sens Pressure sensor or + + ring ring oscilla oscillator tor Supply voltage frequency change change Resistive pressure Sensor #1 (i) Pressures Organic ring oscillator #1 (iii) (ii) Supply Oscillating voltages Supply voltage voltages to ring oscillator (i) → (ii) (i) → (ii) → (iii) -5 100 Supply voltage to ring oscillator (V) -4 Frequency (Hz) 80 Pressure -3 60 sensitivity= -2 40 0.4-13 Hz kPa -1 -1 20 0 0 0 30 60 90 0 30 60 90 Pressure (kPa) Pressure (Pa) action potential Pressure Frequency of potential Receptor Biological sensitivity= mechano- 2-10 Hz kPa -1 receptor Pressure Pressure 22

Mec Mecha hano nose sens nsor ory ne nerves es Biological sensory nerves ∙ V out of ring oscillators connected to the gate of Artificial sensory nerves synaptic transistors Y. Kim  , A. Chortos  , W. Xu  *, Z. Bao*, T.-W. Lee*, et al, Science , 360, 998 (2018) 23

Ar Artificial tificial Peripher eripheral Ner al Nervous ous System System  Neuromorphic Bioelectronics  Materials design for PNS Short-term potentiation Volatile & Fast decay  Low ion doping efficiency  Electric double layer My Approaches • Low ion doping efficiency  Donor-Acceptor polymer Accep Accep Donor Donor tor tor n • Fast decay – Electrical double layer Peak Peak × 1/e Emulation of Signal Transmission in Biology Short Decay time  Naturalistic Sensory and Motor Response • Stretchable – Nanowire Transistors 24