Broadband achromatic metalens design based on machine learning

Feilou Wang; Guangzhou Geng; Xueqian Wang; Junjie Li; Yang Bai; Jianqiang Li; Yongzheng Wen; Bo Li; Jingbo Sun; Ji Zhou

doi:10.1117/12.2658796

8 December 2022 Broadband achromatic metalens design based on machine learning

Feilou Wang, Guangzhou Geng, Xueqian Wang, Junjie Li, Yang Bai, Jianqiang Li, Yongzheng Wen, Bo Li, Jingbo Sun, Ji Zhou

Proceedings Volume 12479, Optical Manipulation and Structured Materials Conference (OMC 2022); 124790E (2022) https://doi.org/10.1117/12.2658796
Event: Optics and Photonics International Congress, 2022, Yokohama, Japan

Abstract

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 meta-surface 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.

Citation Download Citation

Feilou Wang, Guangzhou Geng, Xueqian Wang, Junjie Li, Yang Bai, Jianqiang Li, Yongzheng Wen, Bo Li, Jingbo Sun, and Ji Zhou "Broadband achromatic metalens design based on machine learning", Proc. SPIE 12479, Optical Manipulation and Structured Materials Conference (OMC 2022), 124790E (8 December 2022); https://doi.org/10.1117/12.2658796

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