journal article·2025-04-08

Artificial Synapse of CuBiI4 Memristors

Lingyu Meng, Hanghui Li, Fei Wang, Xianmin Zhang

Publisher page

Abstract

Memristors have been proposed to emulate synaptic activities in neuromorphic systems. In this study, CuBiI 4 was used in the fabrication of memristors, and it was employed for artificial synapse simulation. The memristor exhibited bipolar resistive switching behavior. Furthermore, it was able to simulate the short-term and long-term plasticity of biological synapses, demonstrating its ability to transition from short-term memory to long-term memory. The “Learning, Forgetting, and Relearning” process of the human brain was also emulated. Moreover, the device successfully demonstrated associative learning behavior by mimicking Pavlov’s dog experiment. This study offers valuable insights with the aim of nonvolatile memory storage and biological synaptic simulation.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Perovskite Materials and Applications · Phase-change materials and chalcogenides

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

Affiliations

Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Material Science and Engineering

Northeastern University

Funding

Liaoning Revitalization Talents Program · National Natural Science Foundation of China

References (46)

Show source-provided references
1. 10.1002/adma.20190276110.1002/adma.201902761 ↗
2. 10.1126/sciadv.adg328910.1126/sciadv.adg3289 ↗
3. 10.1016/j.nanoen.2023.10916910.1016/j.nanoen.2023.109169 ↗
4. 10.1002/adma.20200465910.1002/adma.202004659 ↗
5. 10.1002/adma.20231070410.1002/adma.202310704 ↗
6. 10.1002/adma.20240090410.1002/adma.202400904 ↗
7. 10.1016/j.nanoen.2023.10839810.1016/j.nanoen.2023.108398 ↗
8. 10.1126/science.ado174410.1126/science.ado1744 ↗
9. 10.1002/adfm.20191015110.1002/adfm.201910151 ↗
10. 10.1002/adfm.20230521110.1002/adfm.202305211 ↗
11. 10.1002/adfm.20241237510.1002/adfm.202412375 ↗
12. 10.1002/adfm.20230980710.1002/adfm.202309807 ↗
13. 10.1126/science.124398210.1126/science.1243982 ↗
14. 10.1039/D1CP02878B10.1039/D1CP02878B ↗
15. 10.1021/acsnano.2c0310010.1021/acsnano.2c03100 ↗
16. 10.1021/acsami.2c1920110.1021/acsami.2c19201 ↗
17. 10.1038/s41586-023-05825-y10.1038/s41586-023-05825-y ↗
18. 10.1002/adma.20231027910.1002/adma.202310279 ↗
19. 10.1002/adma.20180379210.1002/adma.201803792 ↗
20. 10.1038/s41467-019-08918-310.1038/s41467-019-08918-3 ↗
21. 10.1002/adma.20190032610.1002/adma.201900326 ↗
22. 10.1021/acsnano.0c0890310.1021/acsnano.0c08903 ↗
23. 10.1039/D2TC02643K10.1039/D2TC02643K ↗
24. 10.1002/adfm.20220307210.1002/adfm.202203072 ↗
25. 10.1021/acsnano.4c0321610.1021/acsnano.4c03216 ↗
26. 10.1063/5.019198110.1063/5.0191981 ↗
27. 10.1002/cssc.20180081510.1002/cssc.201800815 ↗
28. 10.1039/D0TC01188F10.1039/D0TC01188F ↗
29. 10.1016/j.tsf.2022.13939710.1016/j.tsf.2022.139397 ↗
30. 10.1021/jacs.2c1190810.1021/jacs.2c11908 ↗
31. 10.1039/D4TC01944J10.1039/D4TC01944J ↗
32. 10.1002/adfm.20170578310.1002/adfm.201705783 ↗
33. 10.1109/TED.2020.302200510.1109/TED.2020.3022005 ↗
34. 10.1002/aelm.20240006710.1002/aelm.202400067 ↗
35. 10.1038/nature0285410.1038/nature02854 ↗
36. 10.1002/adma.20190576410.1002/adma.201905764 ↗
37. 10.1126/science.adc915010.1126/science.adc9150 ↗
38. 10.1038/s41565-020-0724-310.1038/s41565-020-0724-3 ↗
39. 10.1146/annurev.neuro.23.1.64910.1146/annurev.neuro.23.1.649 ↗
40. 10.1016/j.neures.2006.09.01710.1016/j.neures.2006.09.017 ↗
41. 10.1002/adma.20160016610.1002/adma.201600166 ↗
42. 10.1021/acsami.1c0320210.1021/acsami.1c03202 ↗
43. 10.1038/387278a010.1038/387278a0 ↗
44. 10.1002/adfm.20231288510.1002/adfm.202312885 ↗
45. 10.1002/adma.20200778210.1002/adma.202007782 ↗
46. 10.1002/adfm.20230991010.1002/adfm.202309910 ↗