1. Thermally stable threshold selector based on CuAg alloy for energy-efficient memory and neuromorphic computing applications.
- Author
-
Zhou, Xi, Zhao, Liang, Yan, Chu, Zhen, Weili, Lin, Yinyue, Li, Le, Du, Guanlin, Lu, Linfeng, Zhang, Shan-Ting, Lu, Zhichao, and Li, Dongdong
- Subjects
STRAY currents ,THRESHOLD voltage ,DATA warehousing ,THERMAL stability ,MEMRISTORS - 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 SiO
2 -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. Designing efficient selector devices remains a challenge. Here, the authors propose a CuAg alloy-based selector with excellent ON/OFF ratio and thermal stability. It can effectively suppress the sneak-path current in 1S1R arrays, making it suitable for storage class memory and neuromorphic computing applications. [ABSTRACT FROM AUTHOR]- Published
- 2023
- Full Text
- View/download PDF