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Electronic and Photoelectronic Memristors Based on 2D Materials

Kai TangState Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu 610054 ChinaYang WangState Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu 610054 ChinaChuanhui GongState Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu 610054 ChinaChujun YinInstitute of Microelectronics of Chinese Academy of Sciences Beijing 100029 ChinaMiao ZhangState Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu 610054 ChinaXianfu WangState Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu 610054 ChinaJie XiongState Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu 610054 China

2022en

ABI

Annotatsiya

Abstract Next‐generation memristive devices and neuromorphic computing have many fantastic properties in breaking down the memory walls of conventional von Neumann structures. Electronic and photoelectronic memristors are the most important basic components, equipping with the capability of data storage and information processing for electronic and photoelectronic signals. 2D layered materials exhibit many unique physical advantages such as novel mechanisms, ultrathin channel, high mechanical flexibility, and easy electrical control, and thus demonstrate great potential for memory with high density, fast speed, and low power consumption. In recent years, abundant and fruitful designs have been devoted in terms of 2D memristors. Herein, the recent advances of 2D electronic and photoelectronic memristors are reviewed, as well as the application on simulating artificial brain neural network and visual neural network, respectively. An overview of the challenges and perspectives on the exploitation of 2D materials for memristors is given, and routes to realize practical brain and visual neural network are proposed.

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Identifikatorlar

DOI: 10.1002/aelm.202101099

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