Oxide Memristors for Brain-inspired Computing

doi:10.15541/jim20230066

Abstract

Abstract:

Brain-inspired neuromorphic computing refers to simulation of the structure and functionality of the human brain via the integration of electronic or photonic devices. Artificial synapses are the most abundant computation element in the brain-inspired system. Memristors are considered to be ideal devices for artificial synapse applications because of their high scalability and low power consumption. Based on Ohm’s law and Kirchhoff’s law, memristor crossbar arrays can perform parallel multiply-accumulate operations in situ, leading to analogue computing with greatly improved speed and energy efficiency. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with CMOS processes. This work reviews the research progress of oxide memristors for brain-inspired computing, mainly focusing on their resistance switching mechanisms, device structures and performances. These devices fall into three categories: electrical memristors, memristors controlled via both electrical and optical stimuli, and all-optically controlled memristors. The working mechanisms of electrical memristors are commonly related to microstructure change and Joule heat that are detrimental to device stability. The device performance can be improved by optimizing device structure and material composition. Tuning the device conductance with optical signals can avoid microstructure change and Joule heat as well as reducing energy consumption, thus making it possible to address the stability problem. In addition, optically controlled memristors can directly response to external light stimulus enabling integrated sensing-computing-memoring within single devices, which are expected to be used for developing next-generation vision sensors. Hence, the realization of all-optically controlled memristors opens a new window for research and applications of memristors.

Key words: oxide memristor, optoelectronic device, artificial synapse, brain-inspired neuromorphic computing, review

CLC Number:

O472

ZHUGE Xia, ZHU Renxiang, WANG Jianmin, WANG Jingrui, ZHUGE Fei. Oxide Memristors for Brain-inspired Computing[J]. Journal of Inorganic Materials, 2023, 38(10): 1149-1162.

Figures/Tables 5

Fig. 1 Pattern classification using a single-layer perceptron based on TiO2-x memristor[74] (a) Mathematical abstraction of the perceptron; (b) 3×3 binary images; (c) Two sets of images used for classification; (d) Memristive crossbar circuit for the perceptron; (e) Current difference histograms for 50 input images at different training epochs

Fig. 2 Pt/TaOx/TiO2-x/Pt memristor[75] (a) Nonlinear current-voltage curve; (b) Schematic of the sneak path

Fig. 3 Cu/Ta2O5/Pt memristor[108] (a) Typical current-voltage curves; (b-e) Resistive switching mechanism for Set process; (f-i) Resistive switching mechanism for Reset process

Fig. 4 All-optically controlled memristor based on OD-IGZO/OR-IGZO[34] (a) Optical Set behavior upon irradiation with light of various wavelengths; (b) Photocurrent responses to irradiation with light of various wavelengths after blue light irradiation; (c) Synaptic potentiation process of spike-timing dependent plasticity;(d) Synaptic depression process of spike-timing dependent plasticity

Fig. 5 ZnO-based all-optically controlled memristor[35] (a) Photocurrent responses to irradiation with light of various long wavelengths after short-wavelength light irradiation; (b) Nonvolatile logic computing. Colorful figures are available on website

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