journal article·2026-04-01

Highly Stable Chip-Scale Tellurium Memtransistor Based on Field-Induced Oxygen Vacancy Migration

Eunjeong Cho, Seyoung Oh, Ojun Kwon, Min Jeong Kim, Wondeok Seo, Minhee Kim, Shinhoi Kim, Yeongeun Kwon, Yedam Kim, Yujung Kim, Jongwon Yoon, Woojin Park, Byungjin Cho

ACS Applied Materials & Interfaces· Volume 18· Issue 12
Publisher page

Abstract

Memtransistors have recently attracted attention as promising synaptic devices owing to their precise conductance tunability and heterosynaptic functionality, which can enhance the energy efficiency and integration density of neuromorphic systems. However, previous memtransistors have had difficulty controlling the lateral resistive switching characteristics and reproducibility to achieve a high yield and electrical durability. In this study, highly stable chip-scale Te memtransistor switching based on Schottky barrier height modulation from field-induced oxygen vacancy migration in TeO 2– x was demonstrated. The thickness of the radiofrequency (RF)-sputtered polycrystalline Te is a critical parameter in modulating the resistive behavior with gating tunability because the percentage of TeO 2– x in the Te film can be precisely controlled by its thickness. The Te memtransistor device can emulate heterosynaptic plasticity through voltage stimuli to the drain and gate terminals. The statistical distribution of the synaptic plasticity indicated that 62 devices were functional across an 8 × 8 memtransistor array in a single chip, providing a high yield of >96.9%. Additionally, the device exhibited high linearity and low asymmetry during potentiation/depression endurance cycling over 10 000 pulses. Furthermore, high pattern-recognition accuracy (∼94.2%) for handwritten numbers in a simulation task validated this promising method for the synaptic components of neuromorphic circuits. This study contributes significantly to the design diversity and large-scale integration of neuromorphic systems.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Transition Metal Oxide Nanomaterials · Advanced Memory and Neural Computing · Semiconductor materials and devices

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

Affiliations

Department of Advanced Materials Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea

Department of Urban, Energy, and Environmental Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea

Energy and Environment Materials Research Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam 51508, Republic of Korea

Advanced Energy Research Institute, Chungbuk National University, Chungdae-ro 1, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea

Biomedical Research Institute, Chungbuk National University Hospital, 766, 1 Sunhwan-ro, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea

Funding

Ministry of Education · Ministry of Science and ICT, South Korea · National Research Foundation of Korea · Ministry of Trade, Industry and Energy

References (70)

Show source-provided references
1. 10.1145/216585.21658810.1145/216585.216588 ↗
2. 10.1002/adfm.20200677310.1002/adfm.202006773 ↗
3. 10.1126/science.125464210.1126/science.1254642 ↗
4. 10.1088/1741-2560/13/5/05100110.1088/1741-2560/13/5/051001 ↗
5. Indiveri, G.; Corradi, F.; Qiao, N. Neuromorphic architectures for spiking deep neural networks. 2015 IEEE International Electron Devices Meeting (IEDM) ; IEEE, 2015; pp 4.2.1–4.2.4.10.1109/IEDM.2015.7409623 ↗
6. 10.1021/nl904092h10.1021/nl904092h ↗
7. 10.1109/JPROC.2015.244409410.1109/JPROC.2015.2444094 ↗
8. 10.1002/adma.20120368010.1002/adma.201203680 ↗
9. 10.1038/s41378-023-00487-210.1038/s41378-023-00487-2 ↗
10. 10.1002/adfm.20200544310.1002/adfm.202005443 ↗
11. 10.1038/s41578-022-00434-z10.1038/s41578-022-00434-z ↗
12. 10.1038/s41467-020-17850-w10.1038/s41467-020-17850-w ↗
13. 10.1039/C9MH02033K10.1039/C9MH02033K ↗
14. 10.1021/acsnano.4c1288410.1021/acsnano.4c12884 ↗
15. 10.1038/s41467-019-11187-910.1038/s41467-019-11187-9 ↗
16. 10.1021/acsnano.8b0397710.1021/acsnano.8b03977 ↗
17. 10.1002/adfm.20210417410.1002/adfm.202104174 ↗
18. 10.1038/nature2574710.1038/nature25747 ↗
19. 10.1021/acsnano.9b0742110.1021/acsnano.9b07421 ↗
20. 10.1002/adma.20220826610.1002/adma.202208266 ↗
21. 10.1002/adma.20190130010.1002/adma.201901300 ↗
22. 10.1002/smll.20220361110.1002/smll.202203611 ↗
23. 10.1038/s41699-023-00427-810.1038/s41699-023-00427-8 ↗
24. 10.1038/s41467-022-30564-510.1038/s41467-022-30564-5 ↗
25. 10.1002/inf2.1247610.1002/inf2.12476 ↗
26. 10.1038/s41467-025-60697-210.1038/s41467-025-60697-2 ↗
27. 10.1038/s41467-025-61576-610.1038/s41467-025-61576-6 ↗
28. 10.1002/cnma.20240064810.1002/cnma.202400648 ↗
29. 10.1038/s41699-022-00293-w10.1038/s41699-022-00293-w ↗
30. 10.1021/acsnano.3c0355910.1021/acsnano.3c03559 ↗
31. 10.1021/acsnano.4c1048910.1021/acsnano.4c10489 ↗
32. 10.1088/1361-6528/ac544410.1088/1361-6528/ac5444 ↗
33. 10.3390/nano1402020310.3390/nano14020203 ↗
34. 10.1039/D1TC01660A10.1039/D1TC01660A ↗
35. 10.1021/acs.nanolett.7b0171710.1021/acs.nanolett.7b01717 ↗
36. 10.1002/adfm.20190658510.1002/adfm.201906585 ↗
37. 10.1038/s41928-018-0058-410.1038/s41928-018-0058-4 ↗
38. 10.1038/s41467-022-30519-w10.1038/s41467-022-30519-w ↗
39. 10.3390/nano908107510.3390/nano9081075 ↗
40. 10.1186/s11671-022-03699-z10.1186/s11671-022-03699-z ↗
41. 10.1039/D3TC04531E10.1039/D3TC04531E ↗
42. 10.1038/s41699-021-00280-710.1038/s41699-021-00280-7 ↗
43. 10.1109/LED.2022.323070510.1109/LED.2022.3230705 ↗
44. 10.1039/D4TC00530A10.1039/D4TC00530A ↗
45. 10.1021/acsaelm.4c0102710.1021/acsaelm.4c01027 ↗
46. 10.1038/s41467-020-19053-910.1038/s41467-020-19053-9 ↗
47. 10.1109/TED.2020.300888710.1109/TED.2020.3008887 ↗
48. 10.1002/smll.20210317510.1002/smll.202103175 ↗
49. 10.1016/j.nanoen.2018.12.05710.1016/j.nanoen.2018.12.057 ↗
50. 10.1002/adfm.20200368310.1002/adfm.202003683 ↗
51. 10.1002/aelm.20210116110.1002/aelm.202101161 ↗
52. 10.1021/acsaelm.1c0132110.1021/acsaelm.1c01321 ↗
53. 10.1038/s41586-024-07360-w10.1038/s41586-024-07360-w ↗
54. 10.1038/nmat445210.1038/nmat4452 ↗
55. Luo Y.-R. · 200710.1201/9781420007282 ↗
56. 10.1038/sj.npp.130155910.1038/sj.npp.1301559 ↗
57. 10.1146/annurev.physiol.64.092501.11454710.1146/annurev.physiol.64.092501.114547 ↗
58. 10.1038/s41598-022-07505-910.1038/s41598-022-07505-9 ↗
59. 10.1021/acsami.2c1549710.1021/acsami.2c15497 ↗
60. 10.1063/1.512491510.1063/1.5124915 ↗
61. 10.1109/JPROC.2018.279084010.1109/JPROC.2018.2790840 ↗
62. 10.1021/acsaelm.2c0039310.1021/acsaelm.2c00393 ↗
63. 10.1038/nmat475610.1038/nmat4756 ↗
64. 10.1002/adfm.20200789410.1002/adfm.202007894 ↗
65. 10.1002/adma.20160431010.1002/adma.201604310 ↗
66. 10.1021/acsami.1c1832910.1021/acsami.1c18329 ↗
67. 10.1113/jphysiol.2013.25412810.1113/jphysiol.2013.254128 ↗
68. 10.1002/EXP.2022012610.1002/EXP.20220126 ↗
69. 10.1002/adma.20250004910.1002/adma.202500049 ↗
70. 10.1002/adfm.20220574410.1002/adfm.202205744 ↗