Transition Between Digital Switching and Analog Synapse in AgO <sub>x</sub> /SnSe <sub>2</sub> Memristor Through Thickness Engineering
Inspired by biological synapses, memristors are regarded as key components for overcoming the limitations of traditional von Neumann architectures. A major challenge lies in achieving controllable switching between digital and analog functionalities within the same device. In this work, we demonstrate an AgO<sub>x</sub>/SnSe<sub>2</sub>-based memristor in which digital and analog switching behaviors can be modulated by adjusting the AgO<sub>x</sub> layer thickness. The device is fabricated through moderate-temperature thermal oxidation, and the switching transition is likely related to oxygen-vacancy filament evolution influenced by AgO<sub>x</sub> thickness. In the digital switching mode, when AgO<sub>x</sub> layer is thick (75 nm), the device exhibits an abrupt switching characteristic with a high ON/OFF ratio exceeding 10<sup>6</sup>, ultra-low subthreshold swing below 10 mV dec<sup>-1</sup>, 5000 s retention, and air stability under tested conditions. In contrast, with a thinner AgO<sub>x</sub> layer (35 nm), the device shows a gradual resistive switching process, indicative of analog synapse behavior, which could enable synaptic weight modulation and signal recognition, such as electrocardiogram (ECG) signals, within the measured conditions. This work presents a facile thickness engineering strategy for digital-to-analog tuning behaviors and underscores the potential of silver oxide-based memristors for multifunctional memory and neuromorphic computing.
Authors
- Cuicui Luo (ORCID: https://orcid.org/0000-0002-4570-5990)
- Nengjie Huo (ORCID: https://orcid.org/0000-0003-2520-6243)
- Haojian Lin (ORCID: https://orcid.org/0000-0002-4076-5943)
- W. Chen
- Min Huang
- Tao Zhou
- Xueting Liu
Institutions
- South China Normal University (CN)
Publication Details
- Journal
- Small
- Published
- 2026-02-02
- DOI
- https://doi.org/10.1002/smll.202510703
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Basic and Applied Basic Research Foundation of Guangdong Province