Application Prospects and Challenges of Micro-Nano Memristors
Main Article Content
Keywords
nanoscale memristors, neuromorphic computing, hardware security, machine learning
Abstract
This paper reviews the basic structure, material systems, operating principles, and application progress of memristors. First, the typical metal-insulator-metal (MIM) structure of memristors is introduced, along with the characteristics of various insulator materials such as metal oxides, two-dimensional materials, and phase-change materials. Subsequently, research progress in simulating biological synapses, achieving physically non-clonable functions, and enhancing data processing efficiency is outlined across various domains including neuromorphic computing, hardware security, data preprocessing, and multi-level storage. Despite advantages in storage density and computational approach, resistive memory devices currently face challenges such as unstable resistance states and inherent device-to-device variations, limiting their readiness for large-scale practical deployment. Recent efforts in material doping and interface engineering have yielded improvements in device uniformity and stability. However, further optimization of material systems and device structures remains essential to enhance reliability and controllability for future applications.
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