Thermally stable threshold selector based on CuAg alloy for energy-efficient memory and neuromorphic computing applications

Xi Zhou, Liang Zhao (), Chu Yan, Weili Zhen, Yinyue Lin, Le Li, Guanlin Du, Linfeng Lu, Shan-Ting Zhang, Zhichao Lu and Dongdong Li ()
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Xi Zhou: Chinese Academy of Sciences
Liang Zhao: Zhejiang University
Chu Yan: Zhejiang University
Weili Zhen: Chinese Academy of Sciences
Yinyue Lin: Chinese Academy of Sciences
Le Li: Chinese Academy of Sciences
Guanlin Du: Chinese Academy of Sciences
Linfeng Lu: Chinese Academy of Sciences
Shan-Ting Zhang: Chinese Academy of Sciences
Zhichao Lu: Hefei Reliance Memory Ltd., Bldg. F4-11F
Dongdong Li: Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract As a promising candidate for high-density data storage and neuromorphic computing, cross-point memory arrays provide a platform to overcome the von Neumann bottleneck and accelerate neural network computation. In order to suppress the sneak-path current problem that limits their scalability and read accuracy, a two-terminal selector can be integrated at each cross-point to form the one-selector-one-memristor (1S1R) stack. In this work, we demonstrate a CuAg alloy-based, thermally stable and electroforming-free selector device with tunable threshold voltage and over 7 orders of magnitude ON/OFF ratio. A vertically stacked 64 × 64 1S1R cross-point array is further implemented by integrating the selector with SiO2-based memristors. The 1S1R devices exhibit extremely low leakage currents and proper switching characteristics, which are suitable for both storage class memory and synaptic weight storage. Finally, a selector-based leaky integrate-and-fire neuron is designed and experimentally implemented, which expands the application prospect of CuAg alloy selectors from synapses to neurons.

Date: 2023
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DOI: 10.1038/s41467-023-39033-z

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