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

2D SnO_(x)/SnS_(2)的异质结构的忆阻器用于神经形态计算

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(State Key Laboratory of Chemo/Biosensing and Chemometrics,College of Chemistry and Chemical Engineering,Hunan University,Changsha 410082,China;Department of Electrical and Computer Engineering,National University of Singapore,Singapore 117583,Singapore;Joint International Research Laboratory of Information Display and Visualization,School of Electronic Science and Engineering,Southeast University,Nanjing 210096,China;Department of Mechanical Engineering,California State University,Los Angeles,5151 State University Dr,Los Angeles,CA 90032,USA;College of Materials Science and Engineering,Hunan University,Changsha 410082,China;Shenzhen Research Institute,Hunan University,Shenzhen 518000,China)

机构地区：[1]State Key Laboratory of Chemo/Biosensing and Chemometrics,College of Chemistry and Chemical Engineering,Hunan University,Changsha 410082,China [2]Department of Electrical and Computer Engineering,National University of Singapore,Singapore 117583,Singapore [3]Joint International Research Laboratory of Information Display and Visualization,School of Electronic Science and Engineering,Southeast University,Nanjing 210096,China [4]Department of Mechanical Engineering,California State University,Los Angeles,5151 State University Dr,Los Angeles,CA 90032,USA [5]College of Materials Science and Engineering,Hunan University,Changsha 410082,China [6]Shenzhen Research Institute,Hunan University,Shenzhen 518000,China

出　　处：《Science China Materials》2025年第2期581-589,共9页中国科学(材料科学)(英文版)

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)。

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.层状金属二硫化物(LMDs)神经形态忆阻器为解决传统冯·诺依曼架构在速度和能效上的限制,提供了一个有前景的替代方案.然而,由于环境稳定性,晶体LMDs忆阻器的阻变行为和操作电压仍面临挑战,这阻碍了神经网络硬件的开发.在此,我们通过臭氧处理控制氧化,制备了SnO_(x)/SnS_(2)阻变层,优化了其阻变操作特性.该忆阻器表现出低的开关电压(~1 V)、快的开关速度(~20 ns)、高的开关比(10^(2)),并具备模拟突触权重可塑性的能力.通过截面透射电子显微镜和X射线光谱等手段的分析,探测到了阻变开关层中的缺陷和Ti导电丝,这有助于实现均匀开关并减少操作可变性.此外,SnO_(x)/SnS_(2)忆阻器在MNIST手写识别中实现了90%的准确率,并成功地实现了基于忆阻器的图像卷积,展示了SnO_(x)/SnS_(2)忆阻器在神经形态应用中的潜力.

关 键 词：SnO_(x)/SnS_(2) oxidation layered metal dichalcogenides convolutional image processing neuromorphic computing 

分 类 号：TB34[一般工业技术—材料科学与工程] TN60[电子电信—电路与系统]