Issue

Vol. 15 (2023)

Two-Terminal Lithium-Mediated Artificial Synapses with Enhanced Weight Modulation for Feasible Hardware Neural Networks

https://doi.org/10.1007/s40820-023-01035-3
Ji Hyun Baek
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
http://orcid.org/0000-0002-2538-2354
Kyung Ju Kwak
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
Seung Ju Kim
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
http://orcid.org/0009-0000-0405-803X
Jaehyun Kim
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
http://orcid.org/0000-0001-7039-9342
Jae Young Kim
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
http://orcid.org/0000-0002-2289-2025
In Hyuk Im
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
http://orcid.org/0000-0002-8963-7213
Sunyoung Lee
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
Kisuk Kang
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
http://orcid.org/0000-0002-0685-7991
Ho Won Jang
Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea
http://orcid.org/0000-0002-6952-7359

Corresponding Author: Ho Won Jang

hwjang@snu.ac.kr

Nano-Micro Letters, Vol. 15 (2023), Article Number: 69

Abstract

Recently, artificial synapses involving an electrochemical reaction of Li-ion have been attributed to have remarkable synaptic properties. Three-terminal synaptic transistors utilizing Li-ion intercalation exhibits reliable synaptic characteristics by exploiting the advantage of non-distributed weight updates owing to stable ion migrations. However, the three-terminal configurations with large and complex structures impede the crossbar array implementation required for hardware neuromorphic systems. Meanwhile, achieving adequate synaptic performances through effective Li-ion intercalation in vertical two-terminal synaptic devices for array integration remains challenging. Here, two-terminal Au/LixCoO2/Pt artificial synapses are proposed with the potential for practical implementation of hardware neural networks. The Au/LixCoO2/Pt devices demonstrated extraordinary neuromorphic behaviors based on a progressive dearth of Li in LixCoO2 films. The intercalation and deintercalation of Li-ion inside the films are precisely controlled over the weight control spike, resulting in improved weight control functionality. Various types of synaptic plasticity were imitated and assessed in terms of key factors such as nonlinearity, symmetricity, and dynamic range. Notably, the LixCoO2-based neuromorphic system outperformed three-terminal synaptic transistors in simulations of convolutional neural networks and multilayer perceptrons due to the high linearity and low programming error. These impressive performances suggest the vertical two-terminal Au/LixCoO2/Pt artificial synapses as promising candidates for hardware neural networks.


Highlights:


1 The Li-mediated artificial synapses with a vertical two-terminal configuration capable of various synaptic behaviors, including bio-plausible synaptic plasticity, were successfully demonstrated for the first time and thoroughly explored
2 Synaptic characteristics based on the progressive dearth of Li in LixCo2 films are precisely controlled over the weight control spike, achieving extraordinary weight control functionality.
3 In artificial neural network simulation, LixCoO2-based neuromorphic system showed excellent accuracy comparable to the theoretical maximum due to the low nonlinearity and programming error, suggesting feasibility of hardware neural network implementation.

Keywords

Artificial synapse Neuromorphic Li-based Two-terminal Synaptic plasticity
Baek, Ji Hyun, Kyung Ju Kwak, Seung Ju Kim, Jaehyun Kim, Jae Young Kim, In Hyuk Im, Sunyoung Lee, Kisuk Kang, and Ho Won Jang. 2023. “Two-Terminal Lithium-Mediated Artificial Synapses With Enhanced Weight Modulation for Feasible Hardware Neural Networks”. Nano-Micro Letters 15 (March):69. https://doi.org/10.1007/s40820-023-01035-3.
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