journal article·2025-12-17Open access

A Perspective on Tellurium/Selenium-Based Nanomaterials for Neuromorphic Computing

Yuxi Chen, Jiajia Zha, Haoxin Huang, Zhang Liang, Peng Yang, Zhongrui Wang, Chaoliang Tan

ACS Applied Materials & Interfaces· Volume 17· Issue 50

Abstract

The long-standing von Neumann architecture, while foundational to modern computing, intrinsically suffers from data-transfer inefficiency, which imposes severe limits on speed and energy efficiency in artificial intelligence, machine learning, and real-time data processing. Inspired by the remarkable energy efficiency of the human brain, neuromorphic computing seeks to emulate neural architectures through hardware capable of adaptive learning. Although early complementary metal-oxide-semiconductor (CMOS)-based neuromorphic systems captured basic synaptic behaviors, their narrow dynamic ranges and high operating voltages impede their application prospects. Van der Waals (vdW) materials, particularly tellurium (Te) and Selenium (Se), have recently emerged as promising platforms for next-generation neuromorphic devices due to their intriguing electronic and optoelectronic properties including considerable carrier mobilities, broadband photoresponse, and strong coupling between electrical, optical, and mechanical stimuli. These unique properties offer a direct physical analogy to biological synapses, thereby enabling neuromorphic computing. This Perspective introduces the fundamentals of synaptic behavior and neuromorphic computing and highlights the distinctive properties of Te/Se nanomaterials for synaptic devices. Then we discuss critical advances in Te/Se-based memristors, heterostructures, and electronic/optoelectronic synaptic transistors. In the end, we conclude this perspective with a discussion on the remaining challenges and future opportunities in this evolving field.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · 2D Materials and Applications · Neural Networks and Reservoir Computing

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

Affiliations

College of Integrated Circuits and Optoelectronic Chips

Shenzhen Technology University

School of microelectronics

Southern University of Science and Technology

Department of Electrical Engineering

City University of Hong Kong

Department of Biomedical Engineering & Hong Kong Branch of National Precious Metals Material Engineering Research Center (NPMM)

Funding

Research Grants Council, University Grants Committee · Science and Technology Foundation of Shenzhen City · Shenzhen Technology University · Innovation and Technology Commission · City University of Hong Kong · Excellent Young Scientists Fund

References (82)

Show source-provided references
1. 10.1002/adma.20240896910.1002/adma.202408969 ↗
2. 10.1002/adfm.20211197010.1002/adfm.202111970 ↗
3. 10.1002/adma.20230850210.1002/adma.202308502 ↗
4. 10.1038/s41586-024-08253-810.1038/s41586-024-08253-8 ↗
5. 10.1002/admt.20180058910.1002/admt.201800589 ↗
6. 10.1038/s42254-020-0208-210.1038/s42254-020-0208-2 ↗
7. 10.1038/s41928-024-01277-y10.1038/s41928-024-01277-y ↗
8. 10.1080/23746149.2016.125958510.1080/23746149.2016.1259585 ↗
9. 10.1088/1361-6463/aade3f10.1088/1361-6463/aade3f ↗
10. 10.1088/1741-2560/13/5/05100110.1088/1741-2560/13/5/051001 ↗
11. 10.1038/s41586-019-1677-210.1038/s41586-019-1677-2 ↗
12. 10.1002/aisy.20200016210.1002/aisy.202000162 ↗
13. 10.1063/5.017942410.1063/5.0179424 ↗
14. 10.1002/smm2.7000510.1002/smm2.70005 ↗
15. 10.1038/nature0693210.1038/nature06932 ↗
16. 10.1126/sciadv.abg883610.1126/sciadv.abg8836 ↗
17. 10.3866/PKU.WHXB20231000410.3866/PKU.WHXB202310004 ↗
18. 10.1039/C8TC05873C10.1039/C8TC05873C ↗
19. 10.1063/5.023557410.1063/5.0235574 ↗
20. 10.1038/s41467-022-35396-x10.1038/s41467-022-35396-x ↗
21. 10.1002/smll.20231001310.1002/smll.202310013 ↗
22. 10.1146/annurev-neuro-090919-02284210.1146/annurev-neuro-090919-022842 ↗
23. 10.1038/sj.npp.130155910.1038/sj.npp.1301559 ↗
24. 10.1111/j.1467-7687.2004.00331.x10.1111/j.1467-7687.2004.00331.x ↗
25. 10.1146/annurev.neuro.31.060407.12563910.1146/annurev.neuro.31.060407.125639 ↗
26. 10.1007/s00422-002-0359-510.1007/s00422-002-0359-5 ↗
27. 10.1109/TNN.2003.81636910.1109/TNN.2003.816369 ↗
28. 10.1016/0167-2789(91)90236-310.1016/0167-2789(91)90236-3 ↗
29. 10.1038/nature2301110.1038/nature23011 ↗
30. 10.1002/adfm.20180634610.1002/adfm.201806346 ↗
31. 10.1002/smll.20231001310.1002/smll.202310013 ↗
32. 10.1109/MCAS.2022.316633110.1109/MCAS.2022.3166331 ↗
33. Calimera A. · Funct. Neurol. · 2013 · 28 · 191
34. 10.1088/2634-4386/ac0a5b10.1088/2634-4386/ac0a5b ↗
35. 10.1038/s42256-019-0097-110.1038/s42256-019-0097-1 ↗
36. 10.1038/s41467-025-56739-410.1038/s41467-025-56739-4 ↗
37. 10.3389/fncir.2014.0000110.3389/fncir.2014.00001 ↗
38. 10.1063/5.008788010.1063/5.0087880 ↗
39. 10.1038/s41467-024-52062-610.1038/s41467-024-52062-6 ↗
40. 10.1021/acsaem.8b0003210.1021/acsaem.8b00032 ↗
41. 10.1016/j.nanoen.2018.12.00310.1016/j.nanoen.2018.12.003 ↗
42. 10.1021/acsphotonics.9b0069410.1021/acsphotonics.9b00694 ↗
43. 10.1103/PhysRevB.11.218610.1103/PhysRevB.11.2186 ↗
44. 10.1002/smm2.114210.1002/smm2.1142 ↗
45. 10.70401/smd.2025.000610.70401/smd.2025.0006 ↗
46. 10.1038/s41467-023-43034-310.1038/s41467-023-43034-3 ↗
47. 10.1038/s41467-024-46646-510.1038/s41467-024-46646-5 ↗
48. 10.1021/acsnano.8b0342410.1021/acsnano.8b03424 ↗
49. 10.1007/s10854-020-04182-010.1007/s10854-020-04182-0 ↗
50. 10.1038/s41928-018-0058-410.1038/s41928-018-0058-4 ↗
51. 10.1002/adom.20170088410.1002/adom.201700884 ↗
52. 10.1126/sciadv.abf735810.1126/sciadv.abf7358 ↗
53. 10.1021/acsnano.7b0478610.1021/acsnano.7b04786 ↗
54. 10.1021/cr900056b10.1021/cr900056b ↗
55. 10.1021/nn405194e10.1021/nn405194e ↗
56. 10.1002/adma.20070007910.1002/adma.200700079 ↗
57. 10.1038/s41565-019-0585-910.1038/s41565-019-0585-9 ↗
58. 10.1002/adma.20200132910.1002/adma.202001329 ↗
59. 10.1021/acsnano.3c0355910.1021/acsnano.3c03559 ↗
60. 10.1088/1361-6463/adbf2d10.1088/1361-6463/adbf2d ↗
61. 10.1080/14686996.2022.216232310.1080/14686996.2022.2162323 ↗
62. 10.1002/sstr.20240038610.1002/sstr.202400386 ↗
63. 10.1007/s40820-025-01705-410.1007/s40820-025-01705-4 ↗
64. 10.1002/adfm.20200558210.1002/adfm.202005582 ↗
65. 10.1016/j.fmre.2022.06.01910.1016/j.fmre.2022.06.019 ↗
66. 10.1021/acsnano.4c0646710.1021/acsnano.4c06467 ↗
67. 10.1038/nmat475610.1038/nmat4756 ↗
68. 10.1021/acsaelm.3c0143410.1021/acsaelm.3c01434 ↗
69. 10.1126/science.abi633210.1126/science.abi6332 ↗
70. 10.3866/PKU.WHXB20231002910.3866/PKU.WHXB202310029 ↗
71. 10.1002/adma.20221159810.1002/adma.202211598 ↗
72. 10.1002/adfm.20241735810.1002/adfm.202417358 ↗
73. 10.1002/adom.20250137110.1002/adom.202501371 ↗
74. 10.1021/acsnano.0c0312410.1021/acsnano.0c03124 ↗
75. 10.1002/smll.20250543610.1002/smll.202505436 ↗
76. 10.1038/s41467-025-59104-710.1038/s41467-025-59104-7 ↗
77. 10.1038/d41586-020-00592-610.1038/d41586-020-00592-6 ↗
78. 10.1002/aelm.20240026810.1002/aelm.202400268 ↗
79. 10.1002/adfm.20210478210.1002/adfm.202104782 ↗
80. 10.1002/adma.20241965310.1002/adma.202419653 ↗
81. 10.1038/s41586-022-04588-210.1038/s41586-022-04588-2 ↗
82. 10.1038/s41928-021-00683-w10.1038/s41928-021-00683-w ↗