Bias‐Tunable GaN‐Based Dual‐Mode Photodiode Integrating Self‐Powered Sensing and Synaptic Memory for Artificial Vision
Source abstract
Abstract
ABSTRACT Artificial visual perception systems urgently require a single device that simultaneously integrates optical sensing, information processing, and memory. However, the fundamentally different operational mechanisms of photodetection and optical memory present a major challenge to their coexistence in one device. Here, a GaN‐based dual‐mode photodiode (GaN‐DM‐photodiode) switchable between self‐powered photodetector (SPPD) and optoelectronic synapse (OS) modes via bias modulation is designed and fabricated. In SPPD mode, the device exhibits a high specific detectivity ( D* ) of 1.49 × 10 1 2 Jones under 465 nm illumination, along with rapid rise/decay times of 84.95/90.68 ms. In OS mode, it successfully emulates fundamental essential biological synaptic functions, including paired‐pulse facilitation (PPF), short‐term to long‐term memory (STM/LTM) transition, and “learning experience” behavior. Furthermore, circuit‐level demonstration confirms the great potential and distinct advantages of this GaN‐DM‐photodiode for practical applications. This work provides a novel strategy for integrating self‐powered optical sensing and memory functions into a single device, offering a robust foundation for the development of high‐efficiency artificial visual perception systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Neural Networks and Reservoir Computing · Advanced Memory and Neural Computing · 2D Materials and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin China
State Key Laboratory for Semiconductor Physics and Chip Technologies Institute of Semiconductors Chinese Academy of Sciences Beijing China
Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China
Funding
National Natural Science Foundation of China · National Key Research and Development Program of China
References (50)
Show source-provided references
1. 10.1126/sciadv.abn932810.1126/sciadv.abn9328 ↗
2. 10.1038/s43246‐024‐00553‐w10.1038/s43246‐024‐00553‐w ↗
3. 10.1021/acsnano.1c0467610.1021/acsnano.1c04676 ↗
4. 10.1007/s11432‐020‐3227‐110.1007/s11432‐020‐3227‐1 ↗
5. 10.1038/s41377‐023‐01079‐510.1038/s41377‐023‐01079‐5 ↗
6. 10.1038/s41377‐023‐01166‐710.1038/s41377‐023‐01166‐7 ↗
7. 10.1109/LED.2022.314876510.1109/LED.2022.3148765 ↗
8. 10.1088/1361-6528/aa749e10.1088/1361-6528/aa749e ↗
9. 10.1002/smll.20231010710.1002/smll.202310107 ↗
10. 10.1364/PRJ.44151910.1364/PRJ.441519 ↗
11. 10.1002/adom.20210038710.1002/adom.202100387 ↗
12. 10.1109/LED.2019.291458510.1109/LED.2019.2914585 ↗
13. 10.1021/acsami.8b0204310.1021/acsami.8b02043 ↗
14. 10.1109/TED.2015.245339910.1109/TED.2015.2453399 ↗
15. Improvement of Self‐Driven Nanowire‐Based Ultraviolet Photodetectors by Metal–Organic Frameworks for Controlling Humanoid Robots
16. 10.1021/acsnano.1c0164310.1021/acsnano.1c01643 ↗
17. 10.1021/acsanm.3c0204710.1021/acsanm.3c02047 ↗
18. 10.1088/1361‐6463/ac7f6610.1088/1361‐6463/ac7f66 ↗
19. 10.1364/OL.16.00177110.1364/OL.16.001771 ↗
20. 10.1016/j.xcrp.2022.10103710.1016/j.xcrp.2022.101037 ↗
21. 10.1002/adma.20240940610.1002/adma.202409406 ↗
22. 10.1002/adma.20200122710.1002/adma.202001227 ↗
23. 10.1126/sciadv.aay522510.1126/sciadv.aay5225 ↗
24. 10.1002/smtd.20230025110.1002/smtd.202300251 ↗
25. 10.1007/s12274‐022‐4122‐z10.1007/s12274‐022‐4122‐z ↗
26. 10.1039/D2TC01363K10.1039/D2TC01363K ↗
27. 10.1002/advs.20241625910.1002/advs.202416259 ↗
28. 10.1002/adma.20230809010.1002/adma.202308090 ↗
29. 10.1364/OE.53907010.1364/OE.539070 ↗
30. 10.1002/adma.20241603310.1002/adma.202416033 ↗
31. 10.1002/adom.20220110410.1002/adom.202201104 ↗
32. 10.1038/s41377‐026‐02195‐810.1038/s41377‐026‐02195‐8 ↗
33. 10.1038/s41377‐025‐01991‐y10.1038/s41377‐025‐01991‐y ↗
34. 10.1109/LED.2018.288493410.1109/LED.2018.2884934 ↗
35. 10.1021/acsanm.1c0160410.1021/acsanm.1c01604 ↗
36. 10.1007/s11433-016-0150-y10.1007/s11433-016-0150-y ↗
37. 10.1002/adom.20190127610.1002/adom.201901276 ↗
38. 10.1039/D2TC02168D10.1039/D2TC02168D ↗
39. 10.1364/PRJ.48793610.1364/PRJ.487936 ↗
40. 10.1002/adom.20250107810.1002/adom.202501078 ↗
41. 10.1016/j.optlastec.2023.10948010.1016/j.optlastec.2023.109480 ↗
42. 10.1002/aelm.20230078110.1002/aelm.202300781 ↗
43. 10.1088/1361‐6641/ab110510.1088/1361‐6641/ab1105 ↗
44. 10.3390/mi1411204410.3390/mi14112044 ↗
45. 10.3390/cryst1104040310.3390/cryst11040403 ↗
46. 10.1039/C7NR01290J10.1039/C7NR01290J ↗
47. 10.1063/1.495504210.1063/1.4955042 ↗
48. 10.1002/adfm.20200159810.1002/adfm.202001598 ↗
49. 10.1021/acsami.9b1774210.1021/acsami.9b17742 ↗
50. 10.1021/acs.nanolett.1c0188510.1021/acs.nanolett.1c01885 ↗