Quantized conductance in MoS 2 memristors for high-accuracy neuromorphic computing

作者：Ziyi Zhang, Xiaojian Zhu, Lixun Wang, Xiaoyu Ye, Runsheng Gao, Yuejun Zhang, Run‐Wei Li · 发表于：Journal of Physics D Applied Physics · 年份：2025 · DOI：10.1088/1361-6463/adcfae · 被引用次数：4 · 研究领域：Advanced Memory and Neural Computing、Neural Networks and Reservoir Computing、Ferroelectric and Negative Capacitance Devices

Abstract Memristors with conductance modulation that mimic biological synapses are building blocks for high-performance neuromorphic computing architecture. However, the analog conductance states in conventional electrochemical metallization type memristors are often unstable and show large randomness during programming, leading to reduced accuracy. Here, we report a vertically structured Ag/MoS 2 /Au memristor showing stable quantized conductance states for accurate neuromorphic computing. The device exhibits non-volatile bipolar resistive switching with uniform switching voltages and quantized states, achieving 10 quantized conductance states within the range of 0–5G 0 with retention exceeding 10 4 s. The device enables high-accuracy image recognition through artificial neural networks simulations. Using the CIFAR-10 dataset, a ResNet-18 model based on the memristor achieves a recognition accuracy of 89.4%, with enhanced robustness against noise perturbations. Our work provides guidelines for the development of memristive synapses for stable and efficient neuromorphic computing.