MRS Meetings and Events

 

EL05.04.04 2024 MRS Spring Meeting

Multi-Neuron Connection Using Multi-Terminal Floating-Gate Memristor for Unsupervised Learning

When and Where

Apr 23, 2024
5:00pm - 7:00pm

Flex Hall C, Level 2, Summit

Presenter

Co-Author(s)

Mihyang Park1,Woojong Yu1

Sungkyunkwan University1

Abstract

Mihyang Park1,Woojong Yu1

Sungkyunkwan University1
Heterosynaptic plasticity in synapses has been successfully demonstrated by multi-terminal memristor and memtransistor (MT-MEMs)<sup>1,2</sup>. However, these MT-MEMs lack the capability to mimic the membrane potential of neurons in multiple neuronal connections. In this study, we demonstrate a multi-terminal floating-gate memristor (MT-FGMEM) to emulate multi-neuron connections. The variable Fermi level (<i>E</i><sub>F</sub>) in graphene allows the charging and discharging of MT-FGMEM using multiple horizontally spaced electrodes. The MT-FGMEM exhibits a high on/off ratio over 10<sup>5</sup> with a retention time of 1000 seconds, approximately 10,000 times higher than other MT-MEMs. The linear relationship between current (<i>I</i><sub>D</sub>) and floating gate potential (<i>V</i><sub>FG</sub>) in the triode region of the MT-FGMEM allows for accurate spike integration at the neuron membrane. The MT-FGMEM fully mimics the temporal and spatial summation of multi-neuron connections, based on the leaky-integrate-and-fire (LIF) functionality. Our artificial neuron consumes significantly less energy, approximately 100,000 times lower (150 pJ), compared to conventional neurons based on silicon integrated circuits (11.7 μJ). By integrating neurons and synapses using MT-FGMEMs, we successfully emulate spiking neurosynaptic training and classification of directional lines in the visual area one (V1), based on the LIF functionality of neurons and the spike-timing-dependent plasticity (STDP) of synapses. We achieved a learning accuracy of 83.08% on the unlabeled MNIST handwritten dataset in unsupervised learning based on our artificial neurons and synapses.

Keywords

2D materials | graphene

Symposium Organizers

Silvija Gradecak, National University of Singapore
Lain-Jong Li, The University of Hong Kong
Iuliana Radu, TSMC Taiwan
John Sudijono, Applied Materials, Inc.

Symposium Support

Gold
Applied Materials

Session Chairs

Lain-Jong Li
John Sudijono

In this Session

EL05.04.01
Chemical Vapor Deposition of One-Dimensional Van der Waals Material Nb2Se9 assisted by Liquid Precursor

EL05.04.02
Room-Temperature Direct Growth of Transition Metal Dichalcogenide Films via Remote Plasma-Assisted Chemical Vapor Deposition

EL05.04.03
Au Nanoparticle Floating-Gate Memristor Array for Low-Power Neuromorphic System

EL05.04.04
Multi-Neuron Connection Using Multi-Terminal Floating-Gate Memristor for Unsupervised Learning

EL05.04.05
WITHDRAWN (NO REG) 5/20/2024 EL05.04.05 Self-Powered Asymmetric-Geometry 2D Diodes for Sensing Volatile Organic Compounds

EL05.04.06
Synthesis of Te and Sb Doped Black Phosphorus Single Crystals, Oxidation-Resistance and Room-Temperature Gas Sensing Applications

EL05.04.07
WITHDRAWN (NO REG) 5/20/2024 EL05.04.07 Novel Technique to Fabricate Thick CNT based Buckypaper for EMI Shielding Applications

EL05.04.08
Structural and Physical Properties of Two Distinct Two-Dimensional Lead Halides with Intercalated Cu(II): A Comparative Study

EL05.04.09
Van der Waals Interface Engineering for Enhancemen of Semiconductor Device Performance

EL05.04.10
Monolayer MoS2 with Controllable and Localized Micro-Scale Domains of Strain enabled by Spatially Varying Nanotopography

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