Inverse Design of Nanophotonic Color Router Robust to Oblique Incidence
Source abstract
Abstract
Abstract With continued pixel miniaturization, conventional micro‐lenses and absorptive color filters in CMOS image sensors suffer from significant performance degradation and low photon influx as the pixel pitch approaches the diffraction limit. While numerous studies have explored nanophotonic color routers as a solution, most lack a systematic analysis of optical efficiency under broad incident angles, which is critical for practical CIS applications. In this work, an inverse design framework based on automatic differentiation is developed to achieve robustness against incident angle variations. The design maintains a high optical efficiency of 78% on average within a ±12° incident angle range for unpolarized light.The trade‐off relation between the acceptance angle range and the achievable optical efficiency of the device is also identified. The proposed optimized method is then demonstrated and can be further extended to design broadband broad‐angle color routers. These findings pave the way for more efficient and versatile CMOS image sensors, offering substantial improvements over existing methods by ensuring efficient light utilization under realistic illumination conditions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: CCD and CMOS Imaging Sensors · Optical Polarization and Ellipsometry · Advanced Optical Imaging Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea
Graduate School of Artificial Intelligence Semiconductor Hanyang University Seoul 04763 Republic of Korea
Funding
Ministry of Trade, Industry and Energy · Korea Semiconductor Research Consortium · Ministry of Culture, Sports and Tourism · Ministry of Science and ICT, South Korea
References (42)
Show source-provided references
1. 10.1109/MCD.2005.143875110.1109/MCD.2005.1438751 ↗
2. 10.1038/s41566-022-01141-510.1038/s41566-022-01141-5 ↗
3. 10.1038/s41566-019-0394-610.1038/s41566-019-0394-6 ↗
4. 10.1038/s41467-018-06773-210.1038/s41467-018-06773-2 ↗
5. 10.1093/nar/gkz75110.1093/nar/gkz751 ↗
6. 10.1109/JSSC.2020.303419210.1109/JSSC.2020.3034192 ↗
7. M.Ishikawa K.Ogawa T.Komuro I.Ishii presented atIEEE Int. Solid‐State Circuits Conf. Digest of Technical Papers. ISSCC San Francisco CA USA February1999.
8. 10.1186/s10033-018-0238-110.1186/s10033-018-0238-1 ↗
9. I.‐M.Park K. W.Lee J.‐H.Park S. J.Song T. Y.Kim Z.Wu presented at2023 Int. Electron Devices Meeting (IEDM) San Francisco CA USA December2023.
10. 10.1002/adma.20210381510.1002/adma.202103815 ↗
11. 10.1088/2040-8986/ad5dcc10.1088/2040-8986/ad5dcc ↗
12. 10.1039/D4NR00105B10.1039/D4NR00105B ↗
13. 10.1063/1.498367910.1063/1.4983679 ↗
14. 10.1364/OPTICA.38422810.1364/OPTICA.384228 ↗
15. 10.1515/nanoph-2022-006910.1515/nanoph-2022-0069 ↗
16. 10.1002/adpr.20200004810.1002/adpr.202000048 ↗
17. 10.1364/OPTICA.44425510.1364/OPTICA.444255 ↗
18. 10.1021/acs.nanolett.7b0313510.1021/acs.nanolett.7b03135 ↗
19. 10.1021/acsphotonics.9b0004210.1021/acsphotonics.9b00042 ↗
20. X.Yang S.Xiong F.Tan Z.Lin Y.Bao L.Wen Q.Chen B.Li arXiv preprint2024 arXiv:2409.12459.
21. 10.1126/sciadv.adn900010.1126/sciadv.adn9000 ↗
22. 10.1021/acsphotonics.2c0099010.1021/acsphotonics.2c00990 ↗
23. 10.1038/s41467-022-28089-y10.1038/s41467-022-28089-y ↗
24. 10.1016/j.isci.2023.10778810.1016/j.isci.2023.107788 ↗
25. 10.1038/s41467-023-38258-210.1038/s41467-023-38258-2 ↗
26. 10.1002/lpor.20230002710.1002/lpor.202300027 ↗
27. 10.1515/nanoph-2021-071310.1515/nanoph-2021-0713 ↗
28. 10.1515/nanoph-2024-012710.1515/nanoph-2024-0127 ↗
29. 10.1063/5.017658710.1063/5.0176587 ↗
30. 10.1371/journal.pone.009533010.1371/journal.pone.0095330 ↗
31. C.Choi J.Park Y.Lee B.Kim J.Kim S.Kim presented at2023 Int. Electron Devices Meeting (IEDM) San Francisco CA USA December2023.
32. 10.1126/science.aaf664410.1126/science.aaf6644 ↗
33. 10.7551/mitpress/3927.001.000110.7551/mitpress/3927.001.0001 ↗
34. J.Kennedy R.Eberhart Proc. of ICNN'95 ‐ Int. Conf. on Neural Networks Perth WA Australia November1995.
35. 10.1021/acsphotonics.3c0069410.1021/acsphotonics.3c00694 ↗
36. 10.1021/acsphotonics.0c0032710.1021/acsphotonics.0c00327 ↗
37. Paszke A. · Automatic Differentiation in Pytorch, NIPS 2017 Workshop on Autodiff · 2017
38. 10.1007/3-540-10861-010.1007/3-540-10861-0 ↗
39. 10.1364/JOSA.55.00120510.1364/JOSA.55.001205 ↗
40. 10.1149/1.240344010.1149/1.2403440 ↗
41. Y.Kim A. W.Jung S.Kim K.Octavian D.Heo C.Park J.Shin S.Nam C.Park J.Park S.Han J.Lee S.Kim M. S.Jang C. Y.Park arXiv preprint2024 arXiv.2406.12904.
42. 10.1515/nanoph-2024-026910.1515/nanoph-2024-0269 ↗