MRS Meetings and Events


EL07.03.16 2023 MRS Fall Meeting

Enhancing Learning Accuracy with Floating-Gate Memristors in a Multi-Terminal Spiking Neurosynaptic Network

When and Where

Nov 27, 2023
8:00pm - 10:00pm

Hynes, Level 1, Hall A



Hosung Choi1,Woojong Yu1

Sungkyunkwan University1


Hosung Choi1,Woojong Yu1

Sungkyunkwan University1
Spiking neural networks (SNNs) are regarded as a more natural representation of artificial neural networks, distinguishing them from other types of networks. While several studies have been conducted to model SNNs, incorporating multiple connections between neurons has proven challenging due to the limited number of terminals in memristors. In this research, we propose a novel spiking neurosynaptic network that utilizes a multi-terminal floating-gate memristor.<br/>Our memristor, named TRAM (Tunneling Random Access Memory), leverages the change in the Fermi energy level (Ef) of graphene, exhibiting desirable memory characteristics such as a high on/off ratio (&gt;10^5), excellent retention (&gt;10,000 times), strong endurance (&gt;100,000 times), and low energy consumption (120pJ). To emulate synapses and neurons, we adjusted the thickness of the insulating film layer (Al<sub>2</sub>O<sub>3</sub>). A thin layer (3nm) was used for neurons to demonstrate the Leaky Integrate-and-Fire (LIF) characteristic, while a thicker layer (7.5nm) was employed for synapses to exhibit Spike-timing-dependent Plasticity (STDP).<br/>When voltage inputs are applied to multiple synapses, all inputs are transmitted to the neurons. If the cumulative input from the synapses fails to surpass the threshold voltage of a neuron, the neuron slowly discharges its voltage in accordance with its leaky properties. However, if the threshold voltage is exceeded, the neuron fires and generates an output signal to update the weight (conductance) of the synapse. The threshold and feedback voltage of the neuron are created using a comparator.<br/>Through our multi-terminal spiking neurosynaptic network, we achieved a high learning accuracy of up to 83.08% on the unlabeled MNIST handwritten dataset.

Symposium Organizers

Gabriela Borin Barin, Empa
Shengxi Huang, Rice University
Yuxuan Cosmi Lin, TSMC Technology Inc
Lain-Jong Li, The University of Hong Kong

Symposium Support

Montana Instruments

Oxford Instruments WITec
Raith America, Inc.

Session Chairs

Gabriela Borin Barin
Lain-Jong Li

In this Session

Ag-Au-PANI Multilayered Nanowire Network Approach for Visible-To-Infrared Display and Data Encryption

Substitutional Oxygen-Mediated Se-Vacancy Healing in WSe2: Enabling High-Mobility p-Type Field-Effect Transistors.

Charge Transport Characteristics in Non-Van der Waals 2D Transition Metal Nitrides Synthesized via Atomic Substitution Approach

Unusual Properties of TiO2 Nanotube Arrays Formed Anodically in Alkanamide-Based Electrolytes

Chemical Vapor Etching of Silicon Wafer for the Synthesis of Highly Dense and Aligned Sub-5 nm Silicon Nanowires Arrays

Multi-Ion Sensor Based on Carbon Nanotube Fibers for Wearable Electronic Tongue

An Electrostatic Force Microscopy-Based Analysis for Metallic and Semiconducting Carbon Nanotubes

Performance Improvement of Thread Transistor using Carbon Nanotube Composite Thread with Ionic Gel

Development of "Transpiration-Type Thermoelectric Power Generating Paper" using Carbon-Nanotube-Composite Papers Without Need for Heat Source

CNFs/CNT-Prussian Blue/Chitosan Modified Thread Electrode for Non-Invasive Sensor of Glucose and Lactate

View More »

Publishing Alliance

MRS publishes with Springer Nature