Emerging 2D Material‐Based Synaptic Devices: Principles, Mechanisms, Improvements, and Applications

ABSTRACT The von Neumann architecture is encountering challenges, including the “memory wall” and “power wall” due to the separation of memory and central processing units, which imposes a major hurdle on today's massive data processing. Neuromorphic computing, which combines data storage and spatiotemporal computation at the hardware level, represents a computing paradigm that surpasses the traditional von Neumann architecture. Artificial synapses are the basic building blocks of the artificial neural networks capable of neuromorphic computing, and require a high on/off ratio, high durability, low nonlinearity, and multiple conductance states. Recently, two‐dimensional (2D) materials and their heterojunctions have emerged as a nanoscale hardware development platform for synaptic devices due to their intrinsic high surface‐to‐volume ratios and sensitivity to charge transfer at interfaces. Here, the latest progress of 2D material‐based artificial synapses is reviewed regarding biomimetic principles, physical mechanisms, optimization methods, and application scenarios. In particular, there is a focus on how to improve resistive switching characteristics and synaptic plasticity of artificial synapses to meet actual needs. Finally, key technical challenges and future development paths for 2D material‐based artificial neural networks are also explored.