Yangwu Wu
(, ), Sifan Li
(, ), Yun Ji
(, ), Zhengjin Weng
(, ), Houying Xing
(, ), Lester Arauz, Travis Hu, Jinhua Hong
(, ), Kah-Wee Ang, Song Liu
(, )
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引用次数: 0
Abstract
Layered metal dichalcogenides (LMDs) neuromorphic memristor devices offer a promising alternative to conventional von Neumann architectures, addressing speed and energy efficiency constraints. However, challenges remain in controlling resistive switching and operating voltage in crystalline LMD memristors due to environmental stabilization issues, which hinder neural network hardware development. Herein, we introduce an optimization method for memristor operation by controlling oxidation through ozone treatment, creating a SnOx/SnS2 resistive layer. These optimized memristors demonstrate low switching voltages (∼1 V), rapid switching speeds (∼20 ns), high switching ratios (102), and the ability to emulate synaptic weight plasticity. Cross-sectional transmission electron microscopy and energy-dispersive X-ray spectroscopy identified defects and Ti conductive filaments in the resistive switching layer, contributing to uniform switching and minimized operating variation. The device achieved 90% accuracy in MNIST handwritten recognition, and hardware-based image convolution was successfully implemented, showcasing the potential of SnOx/SnS2 memristors for neuromorphic applications.
期刊介绍:
Science China Materials (SCM) is a globally peer-reviewed journal that covers all facets of materials science. It is supervised by the Chinese Academy of Sciences and co-sponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China. The journal is jointly published monthly in both printed and electronic forms by Science China Press and Springer. The aim of SCM is to encourage communication of high-quality, innovative research results at the cutting-edge interface of materials science with chemistry, physics, biology, and engineering. It focuses on breakthroughs from around the world and aims to become a world-leading academic journal for materials science.