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Exploring Low Power and Ultrafast Memristor on p-Type van der Waals SnS.

Xiu Fang LuYishu ZhangNaizhou WangSheng LuoKunling PengLin WangHao ChenWei-Bo GaoXian Hui ChenYang BaoGengchiau LiangKian Ping Loh
Published in: Nano letters (2021)
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.
Keyphrases
  • high density
  • transition metal
  • deep learning
  • machine learning
  • skeletal muscle
  • circulating tumor cells
  • single cell
  • gene expression
  • high intensity
  • genome wide
  • energy transfer
  • aedes aegypti
  • electron transfer