期刊
NANO LETTERS
卷 21, 期 20, 页码 8800-8807出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.1c03169
关键词
low power; ultrafast; SnS; memristor; neuromorphic computing
类别
资金
- Agency for Science, Technology and Research (A*STAR) under its AME-IRG funds on Scalable Growth of Ultrathin Ferroelectric Materials for Memory Technologies [A1983c0035]
The study demonstrates a filament-based memristor using p-type SnS as the resistive switching material, exhibiting superlative metrics such as low power consumption, fast switching speed, high endurance switching cycles, and a large on/off ratio. Chip-level simulations reveal an on-chip learning accuracy of 87.76% for image classifications, attributed to the ultrafast and low energy switching of p-type SnS compared to n-type transition metal dichalcogenides.
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 similar to 0.2 V, a switching speed faster than 1.5 ns, high endurance switching cycles, and an ultralarge on/off ratio of 10(8). The device exhibits a power consumption as low as similar to 100 fJ per switch. Chip-level simulations of the memristor based on 32 x 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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