journal article·2025-09

Negative Photoconductivity in Nanowires/QDs Heterojunction Network for Neuromorphic Visual Perception

Shuwen Xin, Tianyu Wang, Kunpeng Dou, Lingyan Zheng, Liren Wu, Qi Chen, Shaohua Ji, Guanglong Ding, Ramakrishna Seeram, Ye Zhou, Fengyun Wang

Advanced Functional Materials· Volume 35· Issue 38
Publisher page

Abstract

Abstract Neuromorphic visual systems, integrating image information acquisition, processing, and storage, hold significant potential for applications in smart security, healthcare, and other fields. Currently, commercial human visual prosthetic systems primarily utilize Si‐based CMOS logic circuits to simulate synaptic functions. Compared to traditional Si‐based materials, metal oxide nanowires offer unique advantages, such as a larger specific surface area and sustained photoconductivity. However, artificial synaptic devices based on metal oxide nanowires can only achieve unidirectional photoresponse enhancement, posing challenges in mimicking hyperpolarization behavior of cone and rod cells and further developing image classification and recognition functions. Herein, an artificial photo‐synaptic device with negative photoconductivity based on metal oxide nanowires/perovskite quantum dots heterojunction fabricated via low‐cost electrospinning is proposed. The carrier dynamics process is investigated using advanced characterization techniques. It is revealed that the negative photoconductivity is induced by heterojunction‐associated conduction carrier consumption. Based on the device array, the optical encryption function is achieved. The array with adjustable conductivity is used to develop an artificial vision system, reducing redundant data and noise while improving recognition accuracy from 51% to 99%. This study highlights the promising potential of optoelectronic synaptic devices based on metal oxide nanowires and perovskite heterojunctions for artificial visual systems.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · CCD and CMOS Imaging Sensors · Neural dynamics and brain function

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

Affiliations

College of Physics Qingdao University Qingdao 266071 China

College of Information Science and Engineering Ocean University of China Qingdao 266100 China

State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Shenzhen 518060 China

College of Design and Engineering National University of Singapore Singapore 117575 Singapore

Institute for Advanced Study Shenzhen University Shenzhen 518060 China

Funding

Basic and Applied Basic Research Foundation of Guangdong Province · Science, Technology and Innovation Commission of Shenzhen Municipality

References (59)

Show source-provided references
1. 10.1002/adfm.20220909110.1002/adfm.202209091 ↗
2. 10.1016/j.neuron.2017.06.01110.1016/j.neuron.2017.06.011 ↗
3. 10.1038/s41586-021-03453-y10.1038/s41586-021-03453-y ↗
4. 10.1002/adma.20180129110.1002/adma.201801291 ↗
5. 10.1021/acs.nanolett.2c0345310.1021/acs.nanolett.2c03453 ↗
6. 10.1002/adfm.20210610510.1002/adfm.202106105 ↗
7. 10.1002/adma.20231070410.1002/adma.202310704 ↗
8. 10.1038/s41467-024-44723-310.1038/s41467-024-44723-3 ↗
9. 10.1002/adfm.20231265810.1002/adfm.202312658 ↗
10. 10.1002/adfm.20230213510.1002/adfm.202302135 ↗
11. 10.1002/adma.20180396110.1002/adma.201803961 ↗
12. 10.1002/adma.20200207510.1002/adma.202002075 ↗
13. 10.1038/ncomms1457910.1038/ncomms14579 ↗
14. 10.1002/adfm.20200232510.1002/adfm.202002325 ↗
15. Huang C. Y. · Chin. J. Struct. Chem. · 2021 · 40 · 1541
16. 10.1039/D2TC04010G10.1039/D2TC04010G ↗
17. 10.1109/ACCESS.2020.301537210.1109/ACCESS.2020.3015372 ↗
18. 10.1016/0042-6989(68)90027-810.1016/0042-6989(68)90027-8 ↗
19. 10.1523/JNEUROSCI.3965-13.201410.1523/JNEUROSCI.3965-13.2014 ↗
20. 10.1038/296862a010.1038/296862a0 ↗
21. 10.1016/j.exer.2019.10784710.1016/j.exer.2019.107847 ↗
22. 10.1002/adma.20220469810.1002/adma.202204698 ↗
23. 10.1152/jn.01399.200710.1152/jn.01399.2007 ↗
24. 10.1002/adom.20190086510.1002/adom.201900865 ↗
25. 10.1016/j.neuron.2024.11.01510.1016/j.neuron.2024.11.015 ↗
26. 10.1021/acsnano.0c0892110.1021/acsnano.0c08921 ↗
27. 10.1002/adfm.20190199110.1002/adfm.201901991 ↗
28. 10.1002/adfm.20200558210.1002/adfm.202005582 ↗
29. 10.1063/5.004712210.1063/5.0047122 ↗
30. 10.1002/adfm.20211269610.1002/adfm.202112696 ↗
31. 10.1021/acs.jpclett.1c0005710.1021/acs.jpclett.1c00057 ↗
32. 10.1002/adfm.20190199110.1002/adfm.201901991 ↗
33. 10.1016/j.apmt.2021.10122310.1016/j.apmt.2021.101223 ↗
34. 10.1021/acs.nanolett.2c0059910.1021/acs.nanolett.2c00599 ↗
35. 10.1002/adma.20210501710.1002/adma.202105017 ↗
36. 10.1002/smll.20230118610.1002/smll.202301186 ↗
37. 10.1021/acsnano.2c0043910.1021/acsnano.2c00439 ↗
38. 10.1039/D3TC01026K10.1039/D3TC01026K ↗
39. 10.1002/adfm.20231637510.1002/adfm.202316375 ↗
40. 10.1109/LED.2022.314990010.1109/LED.2022.3149900 ↗
41. 10.1039/D2TC04340H10.1039/D2TC04340H ↗
42. 10.1039/C8CE01060A10.1039/C8CE01060A ↗
43. 10.1021/acs.jpclett.8b0360410.1021/acs.jpclett.8b03604 ↗
44. 10.1017/S143192761900584110.1017/S1431927619005841 ↗
45. 10.1007/s40820-022-00989-010.1007/s40820-022-00989-0 ↗
46. 10.1002/adfm.20221389410.1002/adfm.202213894 ↗
47. 10.2976/1.302708910.2976/1.3027089 ↗
48. 10.3390/life1205063810.3390/life12050638 ↗
49. 10.1016/S0165-0173(97)00033-710.1016/S0165-0173(97)00033-7 ↗
50. 10.1113/jphysiol.1967.sp00823910.1113/jphysiol.1967.sp008239 ↗
51. 10.1002/adfm.20220880710.1002/adfm.202208807 ↗
52. 10.1146/annurev.physiol.64.092501.11454710.1146/annurev.physiol.64.092501.114547 ↗
53. 10.1007/s11708-023-0885-510.1007/s11708-023-0885-5 ↗
54. 10.1021/acsami.1c1556510.1021/acsami.1c15565 ↗
55. 10.1039/C9TC06565B10.1039/C9TC06565B ↗
56. 10.1002/adfm.20200159810.1002/adfm.202001598 ↗
57. 10.1021/acscatal.3c0309510.1021/acscatal.3c03095 ↗
58. 10.1103/PhysRevLett.78.139610.1103/PhysRevLett.78.1396 ↗
59. 10.1016/j.cpc.2018.01.01210.1016/j.cpc.2018.01.012 ↗