journal article·2022-03

Retina‐Inspired Color‐Cognitive Learning via Chromatically Controllable Mixed Quantum Dot Synaptic Transistor Arrays

Chanho Jo, Jaehyun Kim, Jee Young Kwak, Sung Min Kwon, Joon Bee Park, Jeehoon Kim, Gyeong‐Su Park, Myung‐Gil Kim, Yong‐Hoon Kim, Sung Kyu Park

Advanced Materials· Volume 34· Issue 12
Publisher page

Abstract

Abstract Artificial photonic synapses are emerging as a promising implementation to emulate the human visual cognitive system by consolidating a series of processes for sensing and memorizing visual information into one system. In particular, mimicking retinal functions such as multispectral color perception and controllable nonvolatility is important for realizing artificial visual systems. However, many studies to date have focused on monochromatic‐light‐based photonic synapses, and thus, the emulation of color discrimination capability remains an important challenge for visual intelligence. Here, an artificial multispectral color recognition system by employing heterojunction photosynaptic transistors consisting of ratio‐controllable mixed quantum dot (M‐QD) photoabsorbers and metal‐oxide semiconducting channels is proposed. The biological photoreceptor inspires M‐QD photoabsorbers with a precisely designed red (R), green (G), and blue (B)‐QD ratio, enabling full‐range visible color recognition with high photo‐to‐electric conversion efficiency. In addition, adjustable synaptic plasticity by modulating gate bias allows multiple nonvolatile‐to‐volatile memory conversion, leading to chromatic control in the artificial photonic synapse. To ensure the viability of the developed proof of concept, a 7 × 7 pixelated photonic synapse array capable of performing outstanding color image recognition based on adjustable wavelength‐dependent volatility conversion is demonstrated.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Photoreceptor and optogenetics research

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

Affiliations

Department of Electrical and Electronics Engineering Chung‐Ang University Seoul 06974 Republic of Korea

Department of Chemistry and Materials Research Center Northwestern University 2145 Sheridan Road Evanston IL 60208 USA

School of Advanced Materials Science and Engineering Sungkyunkwan University Suwon 16419 Republic of Korea

Department of Materials Science and Engineering and Research Institute of Advanced Materials Seoul National University Seoul 08826 Republic of Korea

Funding

National Research Foundation of Korea

References (57)

Show source-provided references
1. 10.1002/adma.20180363710.1002/adma.201803637 ↗
2. 10.1038/s41566-019-0362-110.1038/s41566-019-0362-1 ↗
3. 10.1002/adfm.20170395310.1002/adfm.201703953 ↗
4. 10.1038/s41467-018-07943-y10.1038/s41467-018-07943-y ↗
5. 10.1038/d41586-018-06111-y10.1038/d41586-018-06111-y ↗
6. 10.1021/acs.accounts.8b0055310.1021/acs.accounts.8b00553 ↗
7. 10.1002/adma.20170540010.1002/adma.201705400 ↗
8. 10.1038/s41467-020-19806-610.1038/s41467-020-19806-6 ↗
9. 10.1002/adma.20180288310.1002/adma.201802883 ↗
10. 10.1002/adma.20210243510.1002/adma.202102435 ↗
11. 10.1021/acsami.9b0208610.1021/acsami.9b02086 ↗
12. 10.1002/adma.20180396110.1002/adma.201803961 ↗
13. 10.1002/adma.20170095110.1002/adma.201700951 ↗
14. 10.1002/adfm.20190253810.1002/adfm.201902538 ↗
15. 10.1126/science.aac552310.1126/science.aac5523 ↗
16. 10.1038/nature1457610.1038/nature14576 ↗
17. 10.1002/smll.20180415610.1002/smll.201804156 ↗
18. 10.1002/advs.20210051310.1002/advs.202100513 ↗
19. 10.1002/adfm.20201065510.1002/adfm.202010655 ↗
20. 10.1002/smll.20200521710.1002/smll.202005217 ↗
21. 10.1039/D0NR04950F10.1039/D0NR04950F ↗
22. 10.1126/sciadv.aay522510.1126/sciadv.aay5225 ↗
23. 10.1002/advs.20210074210.1002/advs.202100742 ↗
24. 10.1002/adma.20190689910.1002/adma.201906899 ↗
25. 10.1002/adfm.20200159810.1002/adfm.202001598 ↗
26. 10.1038/s41467-018-07572-510.1038/s41467-018-07572-5 ↗
27. 10.1117/1.JNP.13.02090110.1117/1.JNP.13.020901 ↗
28. 10.1038/nphoton.2012.34510.1038/nphoton.2012.345 ↗
29. 10.1002/adma.20170177210.1002/adma.201701772 ↗
30. 10.1002/aisy.20200010710.1002/aisy.202000107 ↗
31. 10.1002/adfm.20200825910.1002/adfm.202008259 ↗
32. 10.1021/acsnano.9b0034010.1021/acsnano.9b00340 ↗
33. 10.1002/aelm.20190085810.1002/aelm.201900858 ↗
34. 10.1002/smll.20210282010.1002/smll.202102820 ↗
35. 10.1021/acsnano.0c0771910.1021/acsnano.0c07719 ↗
36. 10.1126/sciadv.aax880110.1126/sciadv.aax8801 ↗
37. 10.1002/adma.20210621510.1002/adma.202106215 ↗
38. 10.1002/978111865312810.1002/9781118653128 ↗
39. 10.1016/0042-6989(64)90034-310.1016/0042-6989(64)90034-3 ↗
40. 10.1097/00001756-199806220-0000910.1097/00001756-199806220-00009 ↗
41. 10.1039/C2TC00057A10.1039/C2TC00057A ↗
42. 10.1126/science.117052410.1126/science.1170524 ↗
43. 10.1021/jp072407x10.1021/jp072407x ↗
44. 10.1016/0042-6989(82)90102-X10.1016/0042-6989(82)90102-X ↗
45. 10.1038/nmat325610.1038/nmat3256 ↗
46. Yamasaki R. S. · J. Paint Technol. · 1971 · 43 · 75
47. 10.1021/acsami.0c0117610.1021/acsami.0c01176 ↗
48. 10.1038/nnano.2012.6010.1038/nnano.2012.60 ↗
49. 10.1101/cshperspect.a00570210.1101/cshperspect.a005702 ↗
50. 10.1146/annurev.physiol.64.092501.11454710.1146/annurev.physiol.64.092501.114547 ↗
51. 10.1002/smll.20200724110.1002/smll.202007241 ↗
52. 10.1364/OE.44029910.1364/OE.440299 ↗
53. 10.1021/nn202983n10.1021/nn202983n ↗
54. 10.1021/acsami.1c1433210.1021/acsami.1c14332 ↗
55. 10.1021/acs.nanolett.0c0437010.1021/acs.nanolett.0c04370 ↗
56. 10.1038/nnano.2013.10010.1038/nnano.2013.100 ↗
57. 10.1002/smtd.20210129310.1002/smtd.202101293 ↗