Electronic and Photoelectronic Memristors Based on 2D Materials. (5th January 2022)

• Record Type: Journal Article
• Authors: Tang, Kai; Wang, Yang; Gong, Chuanhui; Yin, Chujun; Zhang, Miao; Wang, Xianfu; Xiong, Jie
• Is Part Of: Advanced Electronic Materials. Volume 8:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-05
• Subjects: 2D layered materials -- electronic memristor -- neuromorphic computing -- photoelectronic memristor -- visual neural network; Materials -- Electric properties -- Periodicals; Materials science -- Periodicals; Magnetic materials -- Periodicals; Electronic apparatus and appliances -- Periodicals; 537
• DOI: 10.1002/aelm.202101099
• Languages: English
• ISSNs: 2199-160X

Abstract

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. Abstract : Rapid development of 2D memristive devices including electronic and photoelectronic memristors is reviewed detailedly focusing on physical and chemical mechanisms as well as material's modification and structural innovation, with highlight of their application in brain‐like computation and visual neural network.