记忆电阻器
冯·诺依曼建筑
瓶颈
计算机科学
材料科学
电阻随机存取存储器
神经形态工程学
非易失性存储器
电子工程
人工神经网络
电气工程
电压
嵌入式系统
工程类
计算机硬件
人工智能
操作系统
作者
Yongyue Xiao,Bei Jiang,Zihao Zhang,Shanwu Ke,Yaoyao Jin,Xin Wen,Cong Ye
标识
DOI:10.1080/14686996.2022.2162323
摘要
With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.
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