{"title":"为 RGB-NIR 感测设计基于深度学习的 Metalens 颜色路由器。","authors":"Hua Mu, Yu Zhang, Zhenyu Liang, Haoqi Gao, Haoli Xu, Bingwen Wang, Yangyang Wang, Xing Yang","doi":"10.3390/nano14231973","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18966,"journal":{"name":"Nanomaterials","volume":"14 23","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of a Deep Learning-Based Metalens Color Router for RGB-NIR Sensing.\",\"authors\":\"Hua Mu, Yu Zhang, Zhenyu Liang, Haoqi Gao, Haoli Xu, Bingwen Wang, Yangyang Wang, Xing Yang\",\"doi\":\"10.3390/nano14231973\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":18966,\"journal\":{\"name\":\"Nanomaterials\",\"volume\":\"14 23\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nanomaterials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.3390/nano14231973\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nanomaterials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.3390/nano14231973","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
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-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.
期刊介绍:
Nanomaterials (ISSN 2076-4991) is an international and interdisciplinary scholarly open access journal. It publishes reviews, regular research papers, communications, and short notes that are relevant to any field of study that involves nanomaterials, with respect to their science and application. Thus, theoretical and experimental articles will be accepted, along with articles that deal with the synthesis and use of nanomaterials. Articles that synthesize information from multiple fields, and which place discoveries within a broader context, will be preferred. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental or methodical details, or both, must be provided for research articles. Computed data or files regarding the full details of the experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. Nanomaterials is dedicated to a high scientific standard. All manuscripts undergo a rigorous reviewing process and decisions are based on the recommendations of independent reviewers.