Review of Memristors for In‐Memory Computing and Spiking Neural Networks

Abstract

The convergence of in‐memory computing (IMC) and neuromorphic architectures offers a promising path toward energy‐efficient, scalable artificial intelligence, particularly for edge and real‐time applications. Memristors, resistive devices with nonvolatile, analog switching, uniquely enable this convergence by serving both as computational memory units for matrix‐vector multiplication and as synaptic elements for spike‐based learning. This review comprehensively explores the physical mechanisms, material classes, and integration strategies of memristors tailored for IMC and spiking neural networks, with emphasis on their implementation in crossbar arrays, synapse‐neuron emulation, and hybrid CMOS circuits. It discusses how memristors facilitate key biological learning rules like STDP and LTP/LTD and examine their deployment in edge artificial intelligence, adaptive robotics, and neuromorphic sensors. Despite their potential, device variability, noise, relaxation, scalability limits, and standardization remain pressing challenges. By synthesizing device‐level insights with architectural innovation and emerging applications, this work outlines a roadmap toward fully integrated, low‐power, and brain‐inspired computing systems.