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.

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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

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article

Transition Between Digital Switching and Analog Synapse in AgO x /SnSe 2 Memristor Through Thickness Engineering

Cuicui Luo, Nengjie Huo, Haojian Lin, W. Chen et al.
Small
Advanced Memory and Neural Computing
article

Transition Between Digital Switching and Analog Synapse in AgO x /SnSe 2 Memristor Through Thickness Engineering

Cuicui Luo, Nengjie Huo, Haojian Lin, W. Chen, Min Huang, Tao Zhou, Xueting Liu
article en

Abstract

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.

Small
South China Normal University (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 69%
Advanced Memory and Neural Computing
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Transition Between Digital Switching and Analog Synapse in AgO <sub>x</sub> /SnSe <sub>2</sub> Memristor Through Thickness Engineering — Cuicui Luo, Nengjie Huo, et al. · Small (2026) | TGRS Research Map | TGRS