Broadband achromatic metalens design based on machine learning
文献类型：会议论文
| 作者 | Wang, Feilou2; Geng, Guangzhou3; Wang, Xueqian4; Li, Junjie3; Bai, Yang4; Li, Jianqiang1; Wen, Yongzheng2; Li, Bo5; Sun, Jingbo2; Zhou, Ji2 |
| 出版日期 | 1905-07-14 |
| 会议日期 | April 18, 2022 - April 22, 2022 |
| 关键词 | Design - Neural networks - Phase modulation - Polarization |
| 卷号 | 12479 |
| DOI | 10.1117/12.2658796 |
| 英文摘要 | The determination of the relation between the phase modulation and the geometric parameters of a single meta-atom, is the most important but also time-consuming part in a metasurface design. Here, by developing a machine learning tool, the design process of a high performance achromatic metalens can be greatly simplified and accelerated. The backpropagation neural network is used to build a library of the phase modulation data with 15753 meta-atoms in less than 1 s. In the experiment, designed metalens has been demonstrated to show a high performance of achromatic focusing and imaging ability in the visible wavelengths from 420 to 640 nm without the polarization dependence. 漏 2022 SPIE. |
| 会议录 | Proceedings of SPIE - The International Society for Optical Engineering
 ; 2022 Optical Manipulation and Structured Materials Conference, OMC 2022
|
| 会议录出版者 | SPIE |
| 学科主题 | Machine Learning |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/59643]  |
| 作者单位 | 1.CAS Key Laboratory of Green Process and Engineering, National Engineering Laboratory for Hydrometallurgical Cleaner Production Technology, Institute of Process Engineering, Chinese Academy of Sciences, Beijing; 100190, China 2.State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing; 100084, China 3.Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing; 100190, China 4.Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing; 100083, China 5.Tsinghua Shenzhen International Graduate School, Shenzhen; 518055, China |
| 推荐引用方式 GB/T 7714 | Wang, Feilou,Geng, Guangzhou,Wang, Xueqian,et al. Broadband achromatic metalens design based on machine learning[C]. 见:. April 18, 2022 - April 22, 2022. |
入库方式： OAI收割
来源：过程工程研究所
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