Bioinspired Dual‐Mode Nanowire Photodetector with High Responsivity for Wide Dynamic Range Image Sensing
Source abstract
Abstract
Abstract Machine vision systems require image sensors possessing a wide dynamic range (DR) and superior responsivity to adapt to vastly varying illumination conditions. While conventional silicon‐based image sensors are inherently limited in DR ‐ often necessitating complex auxiliary circuitry or computational correction, they still fail to capture the full details of natural scenes. Inspired by the complementary functions of rod and cone cells in the human retina, a dual‐mode photodetector based on Cd(S, Se) nanowire with S‐vacancy‐induced trap states is developed. This device exhibits spontaneously switchable transient and persistent photoconductivity behaviors, which simulate the photopic and scotopic vision of the human retina, respectively. This biomimetic device achieves a remarkable responsivity of 3.23 × 10 7 A W −1 , an ultra‐low threshold of 2 × 10 −12 W cm 2 for light detection, and an ultra‐wide DR of 193 dB at 638 nm. When integrated into an imaging system, it enables high‐resolution reflective imaging under ambient light and high‐contrast imaging at ultra‐low illumination, which can effectively improve image recognition accuracy. This adaptive photodetection strategy represents a promising avenue for enhancing machine vision performance across dynamic lighting conditions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Nanowire Synthesis and Applications · CCD and CMOS Imaging Sensors · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing 100081 China
School of Electronic Science and Engineering Xiamen University Xiamen 361005 China
Greater Bay Area Innovation Institute Beijing Institute of Technology Zhuhai 519088 China
Funding
National Natural Science Foundation of China
References (53)
Show source-provided references
1. 10.1126/sciadv.adt652710.1126/sciadv.adt6527 ↗
2. 10.1038/s41928-024-01280-310.1038/s41928-024-01280-3 ↗
3. 10.1038/s41928-022-00778-y10.1038/s41928-022-00778-y ↗
4. 10.1038/s41586-020-2285-x10.1038/s41586-020-2285-x ↗
5. 10.1038/s41467-021-26919-z10.1038/s41467-021-26919-z ↗
6. 10.1088/1674-4926/45/5/05160110.1088/1674-4926/45/5/051601 ↗
7. 10.1002/adfm.20210077310.1002/adfm.202100773 ↗
8. 10.1038/s41928-022-00713-110.1038/s41928-022-00713-1 ↗
9. N.Robidoux D.‐e.Seo F.Ariza L. E. G.Capel A.Sharma F.Heide InProc. 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition—(CVPR)IEEE 3 Park Avenue New York U.S. 2021 6293‐6303.10.1109/CVPR46437.2021.00623 ↗
10. 10.1145/3355089.335650810.1145/3355089.3356508 ↗
11. 10.3390/rs1702019910.3390/rs17020199 ↗
12. 10.1007/s00371-023-03032-410.1007/s00371-023-03032-4 ↗
13. Y.Kim W.Choi D.Park H.Jeoung B.Kim Y.Oh S.Oh B.Park E.Kim Y.Lee T.Jung Y.Kim S.Yoon S.Hong J.Lee S.Jung C.‐R.Moon Y.Park D.Lee D.Chang InProc. 2018 IEEE International Solid‐State Circuits Conf.—(ISSCC) IEEE 3 Park Avenue New York U.S. 2018 84–85.10.1109/ISSCC.2018.8310195 ↗
14. 10.1109/16.62882810.1109/16.628828 ↗
15. 10.1109/JSSC.1984.105208410.1109/JSSC.1984.1052084 ↗
16. 10.1109/TED.2009.203059910.1109/TED.2009.2030599 ↗
17. Chane C. S. · Front. Neurosci. · 2016 · 10 · 391
18. 10.1146/annurev-vision-091517-03405510.1146/annurev-vision-091517-034055 ↗
19. 10.1002/adfm.20170444610.1002/adfm.201704446 ↗
20. 10.1021/acsnano.8b0560210.1021/acsnano.8b05602 ↗
21. 10.1038/nnano.2012.19310.1038/nnano.2012.193 ↗
22. 10.1038/nnano.2014.20710.1038/nnano.2014.207 ↗
23. 10.1088/1674-4926/44/9/09260410.1088/1674-4926/44/9/092604 ↗
24. 10.1088/1674-4926/45/5/05260110.1088/1674-4926/45/5/052601 ↗
25. 10.1038/s41928-022-00713-110.1038/s41928-022-00713-1 ↗
26. 10.1002/lpor.20240219210.1002/lpor.202402192 ↗
27. 10.1038/nnano.2016.3010.1038/nnano.2016.30 ↗
28. 10.1038/nmat263510.1038/nmat2635 ↗
29. 10.1038/nphoton.2013.3210.1038/nphoton.2013.32 ↗
30. 10.1002/aelm.20200092010.1002/aelm.202000920 ↗
31. 10.1002/adma.20241377110.1002/adma.202413771 ↗
32. Liang Y. · Laser Photonics Rev. · 2025 · 19
33. 10.1038/s41377-024-01680-210.1038/s41377-024-01680-2 ↗
34. 10.26599/NR.2025.9490721510.26599/NR.2025.94907215 ↗
35. 10.1007/s00339-020-04056-610.1007/s00339-020-04056-6 ↗
36. 10.1007/s40820-020-0371-010.1007/s40820-020-0371-0 ↗
37. 10.1002/adfm.20230694110.1002/adfm.202306941 ↗
38. 10.1002/adma.20150009910.1002/adma.201500099 ↗
39. 10.1002/adfm.20240752510.1002/adfm.202407525 ↗
40. 10.1007/s40843-022-2155-010.1007/s40843-022-2155-0 ↗
41. 10.1002/adma.20190368710.1002/adma.201903687 ↗
42. 10.1002/smll.20240582010.1002/smll.202405820 ↗
43. 10.1002/smll.20190359910.1002/smll.201903599 ↗
44. 10.1002/smtd.20230002610.1002/smtd.202300026 ↗
45. 10.1002/adma.20190841910.1002/adma.201908419 ↗
46. 10.1021/acsnano.2c0145510.1021/acsnano.2c01455 ↗
47. 10.1038/s41467-025-61361-510.1038/s41467-025-61361-5 ↗
48. 10.1093/nsr/nwac15810.1093/nsr/nwac158 ↗
49. 10.1016/j.ijleo.2019.16411210.1016/j.ijleo.2019.164112 ↗
50. 10.1016/j.ijleo.2020.16562510.1016/j.ijleo.2020.165625 ↗
51. 10.1021/acs.nanolett.4c0154310.1021/acs.nanolett.4c01543 ↗
52. K.Simonyan A.Zisserman in Proc. 2015 International Conference on Learning Representations‐(ICLR) San Diego U. S.2015 1.
53. Y.Lecun L.Bottou Y.Bengio P.Haffner Proc. IEEE IEEE 3 Park Avenue New York U.S. 1998 86 2278.10.1109/5.726791 ↗