神经形态工程学
记忆电阻器
范德瓦尔斯力
材料科学
光电子学
超短脉冲
电阻随机存取存储器
电压
纳米技术
物理
计算机科学
光学
人工神经网络
激光器
量子力学
机器学习
分子
作者
Xiu Fang Lu,Yishu Zhang,Naizhou Wang,Sheng Luo,Kunling Peng,Lin Wang,Hao Chen,Weibo Gao,Xian Hui Chen,Yang Bao,Gengchiau Liang,Kian Ping Loh
出处
期刊:Nano Letters
[American Chemical Society]
日期:2021-10-13
卷期号:21 (20): 8800-8807
被引量:79
标识
DOI:10.1021/acs.nanolett.1c03169
摘要
Memristor devices that exhibit high integration density, fast speed, and low power consumption are candidates for neuromorphic devices. Here, we demonstrate a filament-based memristor using p-type SnS as the resistive switching material, exhibiting superlative metrics such as a switching voltage ∼0.2 V, a switching speed faster than 1.5 ns, high endurance switching cycles, and an ultralarge on/off ratio of 108. The device exhibits a power consumption as low as ∼100 fJ per switch. Chip-level simulations of the memristor based on 32 × 32 high-density crossbar arrays with 50 nm feature size reveal on-chip learning accuracy of 87.76% (close to the ideal software accuracy 90%) for CIFAR-10 image classifications. The ultrafast and low energy switching of p-type SnS compared to n-type transition metal dichalcogenides is attributed to the presence of cation vacancies and van der Waals gap that lower the activation barrier for Ag ion migration.
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