Li Haoze, Gao Qin, Gao Juan, Huang Jiangshun, Geng Xueli, Wang Guoxing, Liang Bo, Li Xinghe, Wang Mei, Xiao Zhisong, Chu Paul K, Huang Anping
School of Physics, Beihang University, Beijing 100191, China.
School of Physics and School of Chemistry, Beihang University, Beijing 100191, China.
ACS Appl Mater Interfaces. 2023 Oct 4;15(39):46449-46459. doi: 10.1021/acsami.3c07179. Epub 2023 Sep 22.
Oxide-based memristors composed of Ag/porous SiO/Si stacks are fabricated using different etching time durations between 0 and 90 s, and the memristive properties are analyzed in the relative humidity (RH) range of 30-60%. The combination of humidity and porous structure provides binding sites to control silver filament formation with a confined nanoscale channel. The memristive properties of devices show high on/off ratios up to 10 and a dispersion coefficient of 0.1% of the high resistance state () when the RH increases to 60%. Humidity-mediated silver ion migration in the porous SiO memristors is investigated, and the mechanism leading to the synergistic effects between the porous structure and environmental humidity is elucidated. The artificial neural network constructed theoretically shows that the recognition rate increases from 60.9 to 85.29% in the RH range of 30-60%. The results and theoretical understanding provide insights into the design and optimization of oxide-based memristors in neuromorphic computing applications.
由银/多孔二氧化硅/硅堆叠组成的氧化物基忆阻器在0至90秒的不同蚀刻时间下制备,并在30%至60%的相对湿度(RH)范围内分析其忆阻特性。湿度和多孔结构的结合提供了结合位点,以通过受限的纳米级通道控制银细丝的形成。当相对湿度增加到60%时,器件的忆阻特性显示出高达10的开/关比和高电阻状态()0.1%的分散系数。研究了多孔二氧化硅忆阻器中湿度介导的银离子迁移,并阐明了导致多孔结构与环境湿度之间协同效应的机制。理论构建的人工神经网络表明,在30%至60%的相对湿度范围内,识别率从60.9%提高到85.29%。这些结果和理论认识为神经形态计算应用中氧化物基忆阻器的设计和优化提供了见解。