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The memristor is a resistive switch where its resistive state is programable based on the applied voltage or current. Memristive devices are thus capable of storing and computing information simultaneously, breaking the Von Neumann bottleneck. Since the first nanomemristor made by Hewlett‐Packard in 2008, advances so far have enabled nanostructured, low‐power, high‐durability devices that exhibit superior performance over conventional CMOS devices. Herein, the development of memristors based on different physical mechanisms is reviewed. In particular, device stability, integration density, power consumption, switching speed, retention, and endurance of memristors, that are crucial for neuromorphic computing, are discussed in detail. An overview of various neural networks with a focus on building a memristor‐based spike neural network neuromorphic computing system is then provided. Finally, the existing issues and challenges in implementing such neuromorphic computing systems are analyzed, and an outlook for brain‐like computing is proposed. [ABSTRACT FROM AUTHOR]

Keywords: memristors; neural networks; neuromorphic computing; reliability; variability EN memristors neural networks neuromorphic computing reliability variability 1 1 1 02/14/23 20230201 NES 230201 B Memristors b Memristive neuromorphic computing promotes the development of humanoid robotics, and its performance is directly affected by the memristors' characteristics. In article number 2200833, Wenbin Chen, Shuo Gao, and co-workers overview the recent progress of memristors with different physical mechanisms and their characteristics. Memristors, neural networks, neuromorphic computing, reliability, variability. [Extracted from the article]