journal article·2025-04

Dual‐Electrolyte Neuromorphic Transistor for Risk Detection and Image Processing

Su‐Kyung Kim, Seung‐Won Choi, Mingyu Kim, Kwang‐Ro Yun, Gunuk Wang, Tae‐Yeon Seong

Advanced Materials Technologies· Volume 10· Issue 7
Publisher page

Abstract

Abstract The human brain is a highly efficient structure that can easily perform various complex tasks, such as shape recognition, presentation, and classification, while consuming minimal energy and occupying only a small volume. This study introduces a bio‐inspired electrolyte‐gated neuromorphic transistor that mimics the functionality of the human brain. A dual‐electrolyte structure combining lithium phosphorus oxynitride and lithium silicate achieves the best performance, with a mobility of 3.1 cm2 V−1 s−1, a paired‐pulse facilitation index of 162.6%, and nonlinearity coefficients of 0.02 and 0.03 (for potentiation and depression, respectively). Further, risk pre‐detection and image recognition are successfully demonstrated using the developed dual‐electrolyte synaptic transistors. A test conducted on the Modified National Institute of Standards and Technology database indicates an accuracy of 91.0%. Thus, the device has the potential to advance artificial vision systems.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · CCD and CMOS Imaging Sensors

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

Affiliations

Department of Material Science and Engineering Korea University Seoul 02841 Republic of Korea

KU‐KIST Graduate School of Converging Science and Technology Seoul 02841 Republic of Korea

References (44)

Show source-provided references
1. 10.1038/s41467-022-32884-y10.1038/s41467-022-32884-y ↗
2. 10.1088/2631-7990/acee2e10.1088/2631-7990/acee2e ↗
3. 10.1039/D3CS00259D10.1039/D3CS00259D ↗
4. 10.1002/adma.20210548510.1002/adma.202105485 ↗
5. 10.1002/adma.20200439810.1002/adma.202004398 ↗
6. 10.1002/aisy.20230002610.1002/aisy.202300026 ↗
7. 10.1002/adom.20230324810.1002/adom.202303248 ↗
8. 10.1039/D3MA00449J10.1039/D3MA00449J ↗
9. 10.1002/adma.20231147210.1002/adma.202311472 ↗
10. 10.1002/adma.20231415610.1002/adma.202314156 ↗
11. 10.1002/adfm.20180402510.1002/adfm.201804025 ↗
12. 10.1021/acsami.6b0886610.1021/acsami.6b08866 ↗
13. 10.1002/adma.20170090610.1002/adma.201700906 ↗
14. 10.1002/adfm.20210004210.1002/adfm.202100042 ↗
15. 10.1002/smtd.20220171910.1002/smtd.202201719 ↗
16. 10.1021/acsami.3c0916210.1021/acsami.3c09162 ↗
17. 10.1002/ange.20191453210.1002/ange.201914532 ↗
18. 10.1016/j.nanoen.2021.10611610.1016/j.nanoen.2021.106116 ↗
19. 10.1038/s41598-019-55310-810.1038/s41598-019-55310-8 ↗
20. 10.1021/acsami.4c0140610.1021/acsami.4c01406 ↗
21. 10.1109/LED.2019.289844310.1109/LED.2019.2898443 ↗
22. 10.1016/j.ssi.2018.07.01110.1016/j.ssi.2018.07.011 ↗
23. 10.1021/acs.chemmater.5b0165410.1021/acs.chemmater.5b01654 ↗
24. 10.1002/adma.20160431010.1002/adma.201604310 ↗
25. 10.1016/j.jallcom.2020.15425310.1016/j.jallcom.2020.154253 ↗
26. 10.1021/acsami.7b0711310.1021/acsami.7b07113 ↗
27. 10.1016/j.ssi.2015.09.02210.1016/j.ssi.2015.09.022 ↗
28. 10.1109/JEDS.2023.326539210.1109/JEDS.2023.3265392 ↗
29. 10.1002/aelm.20190110010.1002/aelm.201901100 ↗
30. 10.1002/admt.20230160410.1002/admt.202301604 ↗
31. 10.1038/s41427-020-0215-x10.1038/s41427-020-0215-x ↗
32. 10.1063/5.004235510.1063/5.0042355 ↗
33. 10.1002/advs.20230344710.1002/advs.202303447 ↗
34. 10.1002/adfm.20231321710.1002/adfm.202313217 ↗
35. 10.1002/aelm.20220060710.1002/aelm.202200607 ↗
36. 10.1002/adma.20210501710.1002/adma.202105017 ↗
37. 10.1002/advs.20220752610.1002/advs.202207526 ↗
38. 10.1021/acsaelm.2c0153910.1021/acsaelm.2c01539 ↗
39. 10.1002/adom.20230008910.1002/adom.202300089 ↗
40. 10.1002/adma.20221152510.1002/adma.202211525 ↗
41. 10.1002/adfm.20230797110.1002/adfm.202307971 ↗
42. 10.1002/adma.20230369910.1002/adma.202303699 ↗
43. 10.1002/aelm.20220091510.1002/aelm.202200915 ↗
44. 10.1002/adfm.20180417010.1002/adfm.201804170 ↗