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用于三维内存计算的自整流忆阻器

Self-Rectifying Memristors for Three-Dimensional In-Memory Computing.

作者信息

Ren Sheng-Guang, Dong A-Wei, Yang Ling, Xue Yi-Bai, Li Jian-Cong, Yu Yin-Jie, Zhou Hou-Ji, Zuo Wen-Bin, Li Yi, Cheng Wei-Ming, Miao Xiang-Shui

机构信息

School of Integrated Circuits, Hubei Key Laboratory of Advanced Memories, Huazhong University of Science and Technology, Wuhan, 430074, China.

Hubei Yangtze Memory Laboratories, Wuhan, 430205, China.

出版信息

Adv Mater. 2024 Jan;36(4):e2307218. doi: 10.1002/adma.202307218. Epub 2023 Nov 27.

DOI:10.1002/adma.202307218
PMID:37972344
Abstract

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 (>10  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.

摘要

在传统冯·诺依曼系统中,数据移动在时间和能量方面成本高昂,而与人工智能相关的新兴信息技术加剧了这一问题。内存计算(IMC)架构旨在解决这一问题。尽管以忆阻器为代表的IMC硬件原型发展迅速且性能良好,但潜电路问题是大规模和高密度交叉阵列中普遍存在的关键且不可避免的挑战,尤其是在三维(3D)集成中。作为潜电路问题的完美解决方案,自整流忆阻器(SRM)因其卓越的集成密度而被提出用于3D集成。迄今为止,SRM在功耗(阿焦耳级别)和可扩展性(>10 Mbit)方面表现良好。此外,配置SRM的3D集成被认为是用于3D IMC的理想硬件平台。本综述聚焦于SRM的进展及其在3D内存、IMC、神经形态计算和硬件安全中的应用。阐述了SRM在不同应用场景中的优点、缺点和优化策略。还讨论了物理机制、制造工艺和外围电路带来的挑战,以及在器件和系统层面的潜在解决方案。

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