journal article·2025-08Open access

BEOL‐Compatible Tellurium Films for Optically Stimulated and Mechanically Deformable Artificial Synapses

Chung Won Lee, Seung Ju Kim, Han‐Kyun Shin, Sang Sub Han, Changhyeon Yoo, Hyo‐Jong Lee, Jung Han Kim, Yeonwoong Jung

Small· Volume 21· Issue 33

Abstract

Abstract The prevailing von Neumann bottleneck has demanded alternatives capable of more efficiently executing massive data in state‐of‐the‐art digital technologies. Mimicking the human brain's operational principles, various artificial synapse devices have emerged, whose fabrications generally require high‐temperature complementary metal‐oxide‐semiconductor (CMOS) processes. Herein, centimeter‐scale tellurium (Te) films‐based optoelectronic synaptic devices are explored by a back‐end‐of‐line (BEOL) compatible low‐temperature (200 °C) chemical vapor deposition (CVD). The CVD‐grown Te films exhibit prominent semiconducting properties such as broadband photo‐responsiveness accompanying a large degree of mechanical deformability. These characteristics coupled with their scalable manufacturability realize a comprehensive set of optically‐stimulated synaptic plasticity; i.e., excitatory postsynaptic current (EPSC), paired‐pulse facilitation (PPF), and short‐to‐long‐term memory conversion, all of which are well preserved even under severe mechanical deformations. A variety of proof‐of‐concept applications for artificial neural networks (ANNs) are demonstrated employing these deformation‐invariant synaptic features; i.e., high‐accuracy (≈90%) pattern recognition, associative learning, and machine learning‐implemented visual perception. The fundamental mechanism for the synaptic operations is discussed in the context of their persistent photoconductivity (PPC) and its associated memory effect. This study highlights high promise of low‐temperature processable semiconductors for emergent neuromorphic architectures with various form factors beyond the conventional CMOS strategy.
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Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Transition Metal Oxide Nanomaterials

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

Affiliations

NanoScience Technology Center University of Central Florida Orlando FL 32826 USA

The Department of Electrical and Computer Engineering University of Southern California Los Angeles CA 90089 USA

Department of Materials Science and Engineering Dong‐A University Busan 49315 South Korea

Department of Materials Science and Engineering University of Central Florida Orlando FL 32816 USA

Department of Electrical and Computer Engineering University of Central Florida Orlando FL 32816 USA

Funding

National Research Foundation · National Science Foundation

References (61)

Show source-provided references
1. 10.1038/s41467-017-00803-110.1038/s41467-017-00803-1 ↗
2. 10.1038/s41565-018-0102-610.1038/s41565-018-0102-6 ↗
3. 10.1038/s41567-018-0101-410.1038/s41567-018-0101-4 ↗
4. 10.1016/j.nanoen.2019.06.03910.1016/j.nanoen.2019.06.039 ↗
5. 10.1002/advs.20190348010.1002/advs.201903480 ↗
6. 10.1039/C9NR04195H10.1039/C9NR04195H ↗
7. 10.1038/s41467-020-17849-310.1038/s41467-020-17849-3 ↗
8. 10.1002/adma.20120368010.1002/adma.201203680 ↗
9. 10.1038/s41467-018-07572-510.1038/s41467-018-07572-5 ↗
10. 10.1038/s41565-019-0501-310.1038/s41565-019-0501-3 ↗
11. 10.1002/adfm.20190370010.1002/adfm.201903700 ↗
12. 10.1002/adma.20200209210.1002/adma.202002092 ↗
13. 10.1021/acsnano.4c0485110.1021/acsnano.4c04851 ↗
14. 10.1016/j.nantod.2025.10263310.1016/j.nantod.2025.102633 ↗
15. 10.1126/science.abj997910.1126/science.abj9979 ↗
16. 10.1002/smll.20190096610.1002/smll.201900966 ↗
17. 10.1002/adfm.20200159810.1002/adfm.202001598 ↗
18. 10.1021/acs.nanolett.1c0324010.1021/acs.nanolett.1c03240 ↗
19. 10.1002/advs.20220212310.1002/advs.202202123 ↗
20. 10.1002/aelm.20210140210.1002/aelm.202101402 ↗
21. 10.1002/adfm.20221146710.1002/adfm.202211467 ↗
22. 10.1038/ncomms1028710.1038/ncomms10287 ↗
23. 10.1515/psr-2021-010810.1515/psr-2021-0108 ↗
24. 10.1002/advs.20210387310.1002/advs.202103873 ↗
25. 10.1039/C7CS00013H10.1039/C7CS00013H ↗
26. 10.1126/science.abi633210.1126/science.abi6332 ↗
27. 10.1002/advs.20240670310.1002/advs.202406703 ↗
28. 10.1021/acsaelm.4c0102710.1021/acsaelm.4c01027 ↗
29. 10.1021/acsnano.4c1048910.1021/acsnano.4c10489 ↗
30. 10.3390/nano1221374710.3390/nano12213747 ↗
31. 10.1039/C4RA11246F10.1039/C4RA11246F ↗
32. 10.1016/j.rinma.2023.10039710.1016/j.rinma.2023.100397 ↗
33. 10.3390/mi1501000110.3390/mi15010001 ↗
34. 10.1038/s41427-021-00314-y10.1038/s41427-021-00314-y ↗
35. 10.1007/s40820-022-00852-210.1007/s40820-022-00852-2 ↗
36. 10.1021/acsnano.8b0342410.1021/acsnano.8b03424 ↗
37. 10.1021/acsami.0c0011610.1021/acsami.0c00116 ↗
38. 10.1002/admi.20190162810.1002/admi.201901628 ↗
39. 10.1038/s41699-020-00162-410.1038/s41699-020-00162-4 ↗
40. 10.1016/S0079-7421(08)60422-310.1016/S0079-7421(08)60422-3 ↗
41. 10.1002/aelm.20220015510.1002/aelm.202200155 ↗
42. 10.1002/admt.20190047110.1002/admt.201900471 ↗
43. 10.1021/acsami.8b0503610.1021/acsami.8b05036 ↗
44. 10.1002/adfm.20150009410.1002/adfm.201500094 ↗
45. Kim K. · Nanomicro. Lett. · 2019 · 14 · 249
46. 10.1016/j.cap.2019.03.01310.1016/j.cap.2019.03.013 ↗
47. 10.1002/aelm.20180071410.1002/aelm.201800714 ↗
48. 10.1016/j.matt.2023.12.03110.1016/j.matt.2023.12.031 ↗
49. 10.1088/2053-1591/3/12/12502110.1088/2053-1591/3/12/125021 ↗
50. 10.1016/j.physb.2011.08.09910.1016/j.physb.2011.08.099 ↗
51. 10.1021/acsaem.4c0128610.1021/acsaem.4c01286 ↗
52. P.‐Y.Chen B.Lin I.‐T.Wang T.‐H.Hou J.Ye S.Vrudhula presented at2015 IEEE/ACM International Conf. on Computer‐Aided Design (ICCAD) Austin TX USA November2015.
53. 10.1002/smll.20230118610.1002/smll.202301186 ↗
54. 10.1101/lm.7070410.1101/lm.70704 ↗
55. 10.1088/0957-4484/25/49/49520410.1088/0957-4484/25/49/495204 ↗
56. 10.1002/smll.20231001310.1002/smll.202310013 ↗
57. 10.1002/pssa.221014025010.1002/pssa.2210140250 ↗
58. 10.1038/nnano.2015.27710.1038/nnano.2015.277 ↗
59. 10.1002/adom.20190115410.1002/adom.201901154 ↗
60. 10.1021/acsnano.7b0576210.1021/acsnano.7b05762 ↗
61. 10.3184/00796740210316537910.3184/007967402103165379 ↗