| Autor: |
Xi Zhou, Liang Zhao, Chu Yan, Weili Zhen, Yinyue Lin, Le Li, Guanlin Du, Linfeng Lu, Shan-Ting Zhang, Zhichao Lu, Dongdong Li |
| Rok vydání: |
2022 |
| DOI: |
10.21203/rs.3.rs-1655986/v1 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
