记忆电阻器
神经形态工程学
冯·诺依曼建筑
可扩展性
计算机科学
电阻随机存取存储器
横杆开关
计算机体系结构
记忆晶体管
路径(计算)
分布式计算
嵌入式系统
计算机硬件
电子工程
电气工程
人工智能
人工神经网络
计算机网络
电信
工程类
操作系统
数据库
电压
作者
Sheng‐Guang Ren,Awei Dong,Yang Ling,Yi‐Bai Xue,Jiancong Li,Yin‐Jie Yu,Houji Zhou,Wenbin Zuo,Yi Li,Wei‐Ming Cheng,Xiangshui Miao
标识
DOI:10.1002/adma.202307218
摘要
Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence. In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor is developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large-scale and high-density crossbar arrays, particularly in three-dimensional (3D) integration. As a perfect solution to the sneak-path issue, a self-rectifying memristor (SRM) is proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (aJ level) and scalability (>102 Mbit). Moreover, SRM-configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.
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