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

Sheng-Guang RenA-Wei DongLing YangYi-Bai XueJian-Cong LiYin-Jie YuHou-Ji ZhouWen-Bin ZuoYi LiWei-Ming ChengXiang-Shui Miao
Published in: Advanced materials (Deerfield Beach, Fla.) (2023)
Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence (AI). In-memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor has 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, self-rectifying memristor (SRM) has been proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (∼aJ) and scalability (> 10 2 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. This article is protected by copyright. All rights reserved.
Keyphrases
  • artificial intelligence
  • high density
  • big data
  • machine learning
  • working memory
  • deep learning
  • physical activity
  • climate change
  • high throughput
  • human health
  • neural network