{"title":"基于深度学习的全息元曲面按需设计及连续相位和振幅调制方法","authors":"Zheyu Hou , Pengyu Zhang , Sixue Chen , Jingjing Wang , Yihang Qiu , Tingting Tang , Chaoyang Li , Jian Shen","doi":"10.1016/j.rinp.2024.108026","DOIUrl":null,"url":null,"abstract":"<div><div>Metasurfaces have shown unique application value in the field of holography due to its outstanding ability to manipulate electromagnetic waves. However, improving the design efficiency and imaging quality remains a challenging task. In this work, we propose a deep learning method that can design holographic metasurface structures on demand, with the Mean Absolute Error (MAE) of 0.04 for both amplitude and phase. We utilize this method to inverse design all-silicon-based metasurfaces operating in the terahertz range, achieving a MAE of 0.015 for two target images. This method not only significantly enhances the design efficiency of holographic metasurfaces but also enables continuous modulation of both phase and amplitude. Consequently, it greatly improves both the design efficiency and imaging quality of holographic metasurfaces, providing a new direction for their design.</div></div>","PeriodicalId":21042,"journal":{"name":"Results in Physics","volume":"66 ","pages":"Article 108026"},"PeriodicalIF":4.4000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On-demand design of holographic metasurfaces and continuous phase and amplitude modulation method based on deep learning\",\"authors\":\"Zheyu Hou , Pengyu Zhang , Sixue Chen , Jingjing Wang , Yihang Qiu , Tingting Tang , Chaoyang Li , Jian Shen\",\"doi\":\"10.1016/j.rinp.2024.108026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Metasurfaces have shown unique application value in the field of holography due to its outstanding ability to manipulate electromagnetic waves. However, improving the design efficiency and imaging quality remains a challenging task. In this work, we propose a deep learning method that can design holographic metasurface structures on demand, with the Mean Absolute Error (MAE) of 0.04 for both amplitude and phase. We utilize this method to inverse design all-silicon-based metasurfaces operating in the terahertz range, achieving a MAE of 0.015 for two target images. This method not only significantly enhances the design efficiency of holographic metasurfaces but also enables continuous modulation of both phase and amplitude. Consequently, it greatly improves both the design efficiency and imaging quality of holographic metasurfaces, providing a new direction for their design.</div></div>\",\"PeriodicalId\":21042,\"journal\":{\"name\":\"Results in Physics\",\"volume\":\"66 \",\"pages\":\"Article 108026\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Results in Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2211379724007113\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Results in Physics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211379724007113","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
On-demand design of holographic metasurfaces and continuous phase and amplitude modulation method based on deep learning
Metasurfaces have shown unique application value in the field of holography due to its outstanding ability to manipulate electromagnetic waves. However, improving the design efficiency and imaging quality remains a challenging task. In this work, we propose a deep learning method that can design holographic metasurface structures on demand, with the Mean Absolute Error (MAE) of 0.04 for both amplitude and phase. We utilize this method to inverse design all-silicon-based metasurfaces operating in the terahertz range, achieving a MAE of 0.015 for two target images. This method not only significantly enhances the design efficiency of holographic metasurfaces but also enables continuous modulation of both phase and amplitude. Consequently, it greatly improves both the design efficiency and imaging quality of holographic metasurfaces, providing a new direction for their design.
Results in PhysicsMATERIALS SCIENCE, MULTIDISCIPLINARYPHYSIC-PHYSICS, MULTIDISCIPLINARY
CiteScore
8.70
自引率
9.40%
发文量
754
审稿时长
50 days
期刊介绍:
Results in Physics is an open access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of physics, materials science, and applied physics. Papers of a theoretical, computational, and experimental nature are all welcome. Results in Physics accepts papers that are scientifically sound, technically correct and provide valuable new knowledge to the physics community. Topics such as three-dimensional flow and magnetohydrodynamics are not within the scope of Results in Physics.
Results in Physics welcomes three types of papers:
1. Full research papers
2. Microarticles: very short papers, no longer than two pages. They may consist of a single, but well-described piece of information, such as:
- Data and/or a plot plus a description
- Description of a new method or instrumentation
- Negative results
- Concept or design study
3. Letters to the Editor: Letters discussing a recent article published in Results in Physics are welcome. These are objective, constructive, or educational critiques of papers published in Results in Physics. Accepted letters will be sent to the author of the original paper for a response. Each letter and response is published together. Letters should be received within 8 weeks of the article''s publication. They should not exceed 750 words of text and 10 references.