Enhancing memristor performance with 2D SnO_(x)/SnS_(2) heterostructure forneuromorphic computing

作     者：Yangwu Wu Sifan Li Yun Ji Zhengjin Weng Houying Xing Lester Arauz Travis Hu Jinhua Hong Kah-Wee Ang Song Liu 吴扬武;李思凡;季云;翁正进;邢厚英;Lester Arauz;Travis Hu;洪金华;Kah-Wee Ang;刘松

作者机构：State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Electrical and Computer EngineeringNational University of SingaporeSingapore 117583Singapore Joint International Research Laboratory of Information Display and VisualizationSchool of Electronic Science and EngineeringSoutheast UniversityNanjing 210096China Department of Mechanical EngineeringCalifornia State UniversityLos Angeles5151 State University DrLos AngelesCA 90032USA College of Materials Science and EngineeringHunan UniversityChangsha 410082China Shenzhen Research InstituteHunan UniversityShenzhen 518000China 

出 版 物：《Science China Materials》 (中国科学(材料科学)(英文版))

年 卷 期：2025年第68卷第2期

页      面：581-589页

核心收录：

学科分类：080903[工学-微电子学与固体电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (22175060 and 12304082) Shenzhen Science and Technology Program (JCYJ20220530160407016) the Natural Science Foundation of Hunan Province (2023JJ20001) the support from the U.S. National Science Foundation (2004251) 

主　　题：SnO_(x)/SnS_(2) oxidation layered metal dichalcogenides convolutional image processing neuromorphic computing 

摘      要：Layered metal dichalcogenides (LMDs) neuromorphic memristor devices offer a promising alternative toconventional von Neumann architectures, addressing speedand energy efficiency constraints. However, challenges remainin controlling resistive switching and operating voltage incrystalline LMD memristors due to environmental stabilization issues, which hinder neural network hardware development. Herein, we introduce an optimization method formemristor operation by controlling oxidation through ozonetreatment, creating a SnO_(x)/SnS_(2) resistive layer. These optimized memristors demonstrate low switching voltages (~1 V),rapid switching speeds (~20 ns), high switching ratios (10^(2)),and the ability to emulate synaptic weight plasticity. Crosssectional transmission electron microscopy and energy-dispersive X-ray spectroscopy identified defects and Ti conductive filaments in the resistive switching layer, contributingto uniform switching and minimized operating variation. Thedevice achieved 90% accuracy in MNIST handwritten recognition, and hardware-based image convolution was successfully implemented, showcasing the potential of SnO_(x)/SnS_(2)memristors for neuromorphic applications.