ReSe2-Based RRAM and Circuit-Level Model for Neuromorphic Computing

Yifu Huang,Ruijing Ge,Yao-Feng Chang,Deji Akinwande,Yuqian Gu,Jiahan Zhang,Jack C Lee,Xiyu Wang,Xiaohan Wu

doi:10.3389/fnano.2021.782836

Abstract

Resistive random-access memory (RRAM) devices have drawn increasing interest for the simplicity of its structure, low power consumption and applicability to neuromorphic computing. By combining analog computing and data storage at the device level, neuromorphic computing system has the potential to meet the demand of computing power in applications such as artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). Monolayer rhenium diselenide (ReSe2), as a two-dimensional (2D) material, has been reported to exhibit non-volatile resistive switching (NVRS) behavior in RRAM devices with sub-nanometer active layer thickness. In this paper, we demonstrate stable multiple-step RESET in ReSe2 RRAM devices by applying different levels of DC electrical bias. Pulse measurement has been conducted to study the neuromorphic characteristics. Under different height of stimuli, the ReSe2 RRAM devices have been found to switch to different resistance states, which shows the potentiation of synaptic applications. Long-term potentiation (LTP) and depression (LTD) have been demonstrated with the gradual resistance switching behaviors observed in long-term plasticity programming. A Verilog-A model is proposed based on the multiple-step resistive switching behavior. By implementing the LTP/LTD parameters, an artificial neural network (ANN) is constructed for the demonstration of handwriting classification using Modified National Institute of Standards and Technology (MNIST) dataset.

Highlights

With the rapid development of artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT), novel computing technology for information processing is becoming crucial (Moh and Raju, 2018; Mohanta et al, 2020; Zhu et al, 2020)
Neuromorphic computing, which is inspired by the operation of human brains, has been proposed as a promising computing paradigm to overcome the bottleneck of von Neumann architecture (Kuzum et al, 2013)
Vertical crossbar MIM structure Resistive random-access memory (RRAM) devices based on 2D ReSe2 have been found to exhibit excellent characteristics, e.g., stable multiple-step RESET when different levels of DC bias are applied

Summary

Introduction

With the rapid development of artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT), novel computing technology for information processing is becoming crucial (Moh and Raju, 2018; Mohanta et al, 2020; Zhu et al, 2020). Conventional circuits based on von Neumann architecture are facing challenges including physical separation of computing units and memory, and low density of on-chip memories, which in turn lead to high energy consumption and low operation efficiency (Du Nguyen et al, 2017; Zidan et al, 2018; Amirsoleimani et al, 2020; Sebastian et al, 2020). Neuromorphic computing, which is inspired by the operation of human brains, has been proposed as a promising computing paradigm to overcome the bottleneck of von Neumann architecture (Kuzum et al, 2013). To physically realize the design of neuromorphic computing circuits, development of novel devices that can directly mimic brain-like long-term potentiation and depression (LTP/LTD) behaviors arise significant interest. Resistive random-access memory (RRAM), known as memristors, is one of the most competitive candidates for the application of neuromorphic computing (Hong X. et al, 2018).

Methods

Results

Conclusion