记忆电阻器
神经形态工程学
电阻随机存取存储器
计算机科学
路径(计算)
横杆开关
电子工程
还原(数学)
超调(微波通信)
非易失性存储器
计算机硬件
电气工程
电压
工程类
电信
人工智能
人工神经网络
几何学
数学
程序设计语言
作者
Yoon‐Seok Lee,Beomki Jeon,Youngboo Cho,Jihyung Kim,Wonbo Shim,Sungjun Kim
标识
DOI:10.1002/admt.202400585
摘要
Abstract Memristors have diverse potential for improving data storage through linear memory control and synaptic operation in AI and neuromorphic computing. Prior research on optimizing memristors in next‐generation devices has generally indicated that emerging arrays and vertical structures can improve memory density, although special fabrication steps are required to realize improved operation. Until now, many obstructions, such as the sneak path current and forming processes from the initial device in array structure operation at the device level, have limited the development of array‐based memristor devices for further progressing circuits and integrated design. In this paper, memristor array studies are examined that have suggested solutions for sneak path current and forming operation problems at the device level. Ultimately, representative solutions are proposed to progress memristors into array structures by introducing the latest research on one diode‐one RRAM (1D1R), one selector‐one RRAM (1S1R), overshoot suppressed RRAM (OSRRAM), self‐rectifying cell (SRC), charge trap memory (CTM) and their applications. Additionally, essential details demonstrating the practical implementation of these devices in crossbar array memory are investigated. Finally, the advantages and perspectives of these array‐based memristor solutions are summarized.
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