journal article·2026-02-02Open access

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

Tao Zhou, Xiaoyu Wang, Xueting Liu, Min Huang, Wenjie Chen, Chengming Luo, Haojian Lin, Nengjie Huo

Abstract

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 x /SnSe 2 ‐based memristor in which digital and analog switching behaviors can be modulated by adjusting the AgO x 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 x thickness. In the digital switching mode, when AgO x layer is thick (75 nm), the device exhibits an abrupt switching characteristic with a high ON/OFF ratio exceeding 10 6 , ultra‐low subthreshold swing below 10 mV dec −1 , 5000 s retention, and air stability under tested conditions. In contrast, with a thinner AgO x 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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Transition Metal Oxide Nanomaterials · Ferroelectric and Negative Capacitance Devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Guangdong Provincial Key Laboratory of Chip and Integration Technology School of Electronic Science and Engineering (School of Microelectronics) South China Normal University Foshan P. R. China

Funding

Basic and Applied Basic Research Foundation of Guangdong Province · National Natural Science Foundation of China

References (47)

Show source-provided references
1. 10.1007/s12596‐022‐01058‐w10.1007/s12596‐022‐01058‐w ↗
2. 10.1038/s41586‐023‐05759‐510.1038/s41586‐023‐05759‐5 ↗
3. 10.1038/s41467‐018‐04484‐210.1038/s41467‐018‐04484‐2 ↗
4. 10.1002/aelm.20230083910.1002/aelm.202300839 ↗
5. 10.1002/adma.20230850210.1002/adma.202308502 ↗
6. 10.1021/acsnano.0c0116410.1021/acsnano.0c01164 ↗
7. 10.1038/s41928‐020‐00513‐510.1038/s41928‐020‐00513‐5 ↗
8. 10.1038/s41578‐022‐00434‐z10.1038/s41578‐022‐00434‐z ↗
9. 10.1038/nature0693210.1038/nature06932 ↗
10. 10.1038/s41928‐020‐00473‐w10.1038/s41928‐020‐00473‐w ↗
11. 10.1002/advs.20230132310.1002/advs.202301323 ↗
12. 10.1038/s41467‐024‐45670‐910.1038/s41467‐024‐45670‐9 ↗
13. 10.1021/acsaelm.4c0042810.1021/acsaelm.4c00428 ↗
14. 10.1002/aisy.20200010510.1002/aisy.202000105 ↗
15. 10.1038/s41467‐024‐46372‐y10.1038/s41467‐024‐46372‐y ↗
16. 10.1038/nmat475610.1038/nmat4756 ↗
17. 10.1039/D5TC01467K10.1039/D5TC01467K ↗
18. 10.1109/ACCESS.2020.303218810.1109/ACCESS.2020.3032188 ↗
19. 10.1016/j.nanoen.2022.10717110.1016/j.nanoen.2022.107171 ↗
20. 10.1021/acsaelm.3c0146210.1021/acsaelm.3c01462 ↗
21. 10.4236/jcc.2018.6900110.4236/jcc.2018.69001 ↗
22. 10.3390/electronics706008010.3390/electronics7060080 ↗
23. 10.1021/acs.jpclett.2c0190610.1021/acs.jpclett.2c01906 ↗
24. 10.1038/s41928‐018‐0023‐210.1038/s41928‐018‐0023‐2 ↗
25. 10.1038/s41467‐020‐17215‐310.1038/s41467‐020‐17215‐3 ↗
26. 10.1063/5.009128610.1063/5.0091286 ↗
27. 10.1021/acsami.8b0574910.1021/acsami.8b05749 ↗
28. 10.1063/5.012820010.1063/5.0128200 ↗
29. 10.1038/s41467‐022‐35160‐110.1038/s41467‐022‐35160‐1 ↗
30. 10.1002/adfm.20211199610.1002/adfm.202111996 ↗
31. 10.1002/adma.20230106310.1002/adma.202301063 ↗
32. 10.1038/s41586‐020‐1942‐410.1038/s41586‐020‐1942‐4 ↗
33. 10.1038/s41467‐022‐29411‐410.1038/s41467‐022‐29411‐4 ↗
34. 10.3788/IRLA2024022410.3788/IRLA20240224 ↗
35. 10.1002/adma.20180154810.1002/adma.201801548 ↗
36. 10.1126/sciadv.150132610.1126/sciadv.1501326 ↗
37. D.Tyulmankov “Computational Models of Learning and Synaptic Plasticity ” arXiv (2024): arXiv:2412.05501 https://doi.org/10.48550/arXiv.2412.05501.
38. 10.1021/acsami.3c1962410.1021/acsami.3c19624 ↗
39. 10.1002/adma.20230809010.1002/adma.202308090 ↗
40. 10.1002/adfm.20210713110.1002/adfm.202107131 ↗
41. 10.1002/adma.20230809010.1002/adma.202308090 ↗
42. 10.1002/adma.20230809010.1002/adma.202308090 ↗
43. 10.1016/j.apsusc.2023.15622110.1016/j.apsusc.2023.156221 ↗
44. 10.3390/bioengineering1103021910.3390/bioengineering11030219 ↗
45. 10.3390/s2006179610.3390/s20061796 ↗
46. 10.1155/2014/17843610.1155/2014/178436 ↗
47. 10.1016/j.procs.2020.04.07410.1016/j.procs.2020.04.074 ↗