Memristors Based on 2D Materials: Bridging Device Theory and Potential Applications

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Abstract
In response to the performance bottlenecks of the von Neumann architectures, in‐memory computing (IMC) has emerged as a promising paradigm and is gaining significant attention. Memristors can leverage their resistance states to perform analog multiply‐accumulate (MAC) operations and provide nonlinear outputs through threshold switching behavior, making them a highly promising hardware platform for computing‐in‐memory architectures. Recently, 2D materials, with their unique physical properties, have attracted considerable interest for memristor applications. This review systematically categorizes the diverse electrical behaviors of 2D material‐based memristors and explores the underlying physical mechanisms. Key performance metrics are also evaluated, highlighting representative and breakthrough values reported in recent literature. The influence of various parameters on device performance is analyzed to guide optimization efforts. Moreover, various current array architectures integrated with selectors, and application progress in artificial neurons, artificial neural networks, logic circuits, and security fields, are summarized. Finally, the development prospects of 2D material‐based memristors are outlined, and key challenges that hinder practical implementation are identified. This overview aims to provide a comprehensive understanding of the field and to inspire further research toward commercial viability.
Funding Information
  • National Natural Science Foundation of China (11904108)
  • Guangdong Provincial Pearl River Talents Program (2019ZT08X639)
  • Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515030107)

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