记忆电阻器
神经形态工程学
多样性(控制论)
计算机体系结构
纳米技术
计算机数据存储
计算机科学
材料科学
人工神经网络
工程类
人工智能
电子工程
计算机硬件
作者
Yang Li,Cheng Zhang,Zhiming Shi,Chunlan Ma,Jun Wang,Qichun Zhang
标识
DOI:10.1007/s40843-021-1771-5
摘要
Memristors have recently emerged as promising contenders for in-memory computing and artificial neural networks, attributed to their analogies to biological synapses and neurons in structural and electrical behaviors. From the diversity level, a variety of materials have been demonstrated to have great potential for memristor applications. Herein, we focus on one class of crystalline materials (CMs)-based flexible memristors with state-of-the-art experimental demonstrations. Firstly, the typical device structure and switching mechanisms are introduced. Secondly, the recent advances on CMs-based flexible memristors, including 2D materials, metal-organic frameworks, covalent organic frameworks, and perovskites, as well as their applications for data storage and neuromorphic devices are comprehensively summarized. Finally, the future challenges and perspectives of CMs-based flexible memristors are presented.
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