Design of a Deep Learning-Based Metalens Color Router for RGB-NIR Sensing
Metalens can achieve arbitrary light modulation by controlling the amplitude, phase, and polarization of the incident waves and have been applied across various fields. This paper presents a color router designed based on metalens, capable of effectively separating spectra from visible light to near...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/14/23/1973 |
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| _version_ | 1850260566674767872 |
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| author | Hua Mu Yu Zhang Zhenyu Liang Haoqi Gao Haoli Xu Bingwen Wang Yangyang Wang Xing Yang |
| author_facet | Hua Mu Yu Zhang Zhenyu Liang Haoqi Gao Haoli Xu Bingwen Wang Yangyang Wang Xing Yang |
| author_sort | Hua Mu |
| collection | DOAJ |
| description | Metalens can achieve arbitrary light modulation by controlling the amplitude, phase, and polarization of the incident waves and have been applied across various fields. This paper presents a color router designed based on metalens, capable of effectively separating spectra from visible light to near-infrared light. Traditional design methods for meta-lenses require extensive simulations, making them time-consuming. In this study, we propose a deep learning network capable of forward prediction across a broad wavelength range, combined with a particle swarm optimization algorithm to design metalens efficiently. The simulation results align closely with theoretical predictions. The designed color router can simultaneously meet the theoretical transmission phase of the target spectra, specifically for red, green, blue, and near-infrared light, and focus them into designated areas. Notably, the optical efficiency of this design reaches 40%, significantly surpassing the efficiency of traditional color filters. |
| format | Article |
| id | doaj-art-bca09bb9af2a40eb88a1db2c9b5950f0 |
| institution | OA Journals |
| issn | 2079-4991 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj-art-bca09bb9af2a40eb88a1db2c9b5950f02025-08-20T01:55:37ZengMDPI AGNanomaterials2079-49912024-12-011423197310.3390/nano14231973Design of a Deep Learning-Based Metalens Color Router for RGB-NIR SensingHua Mu0Yu Zhang1Zhenyu Liang2Haoqi Gao3Haoli Xu4Bingwen Wang5Yangyang Wang6Xing Yang7State Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaState Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaState Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaState Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaState Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaState Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaState Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaState Key Laboratory of Pulsed Power Laser Technology, College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaMetalens can achieve arbitrary light modulation by controlling the amplitude, phase, and polarization of the incident waves and have been applied across various fields. This paper presents a color router designed based on metalens, capable of effectively separating spectra from visible light to near-infrared light. Traditional design methods for meta-lenses require extensive simulations, making them time-consuming. In this study, we propose a deep learning network capable of forward prediction across a broad wavelength range, combined with a particle swarm optimization algorithm to design metalens efficiently. The simulation results align closely with theoretical predictions. The designed color router can simultaneously meet the theoretical transmission phase of the target spectra, specifically for red, green, blue, and near-infrared light, and focus them into designated areas. Notably, the optical efficiency of this design reaches 40%, significantly surpassing the efficiency of traditional color filters.https://www.mdpi.com/2079-4991/14/23/1973deep learningmetalenscolor filterCMOS image sensor |
| spellingShingle | Hua Mu Yu Zhang Zhenyu Liang Haoqi Gao Haoli Xu Bingwen Wang Yangyang Wang Xing Yang Design of a Deep Learning-Based Metalens Color Router for RGB-NIR Sensing Nanomaterials deep learning metalens color filter CMOS image sensor |
| title | Design of a Deep Learning-Based Metalens Color Router for RGB-NIR Sensing |
| title_full | Design of a Deep Learning-Based Metalens Color Router for RGB-NIR Sensing |
| title_fullStr | Design of a Deep Learning-Based Metalens Color Router for RGB-NIR Sensing |
| title_full_unstemmed | Design of a Deep Learning-Based Metalens Color Router for RGB-NIR Sensing |
| title_short | Design of a Deep Learning-Based Metalens Color Router for RGB-NIR Sensing |
| title_sort | design of a deep learning based metalens color router for rgb nir sensing |
| topic | deep learning metalens color filter CMOS image sensor |
| url | https://www.mdpi.com/2079-4991/14/23/1973 |
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