神经形态工程学
记忆电阻器
冯·诺依曼建筑
瓶颈
计算机体系结构
计算机科学
纳米技术
人工智能
材料科学
人工神经网络
电子工程
工程类
嵌入式系统
操作系统
作者
Songtao Ling,Cheng Zhang,Chunlan Ma,Yang Li,Qichun Zhang
标识
DOI:10.1002/adfm.202208320
摘要
Abstract Confronted by the difficulties of the von Neumann bottleneck and memory wall, traditional computing systems are gradually inadequate for satisfying the demands of future data‐intensive computing applications. Recently, memristors have emerged as promising candidates for advanced in‐memory and neuromorphic computing, which pave one way for breaking through the dilemma of current computing architecture. Till now, varieties of functional materials have been developed for constructing high‐performance memristors. Herein, the review focuses on the emerging 2D MXene materials‐based memristors. First, the mainstream synthetic strategies and characterization methods of MXenes are introduced. Second, the different types of MXene‐based memristive materials and their widely adopted switching mechanisms are overviewed. Third, the recent progress of MXene‐based memristors for data storage, artificial synapses, neuromorphic computing, and logic circuits is comprehensively summarized. Finally, the challenges, development trends, and perspectives are discussed, aiming to provide guidelines for the preparation of novel MXene‐based memristors and more engaging information technology applications.
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