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Title: Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning
Authors: Zhu, Ruichao
Qiu, Tianshuo
Wang, Jiafu
Sui, Sai
Hao, Chenglong 
Liu, Tonghao
Li, Yongfeng
Feng, Mingde
Zhang, Anxue
Qiu, Cheng-Wei 
Qu, Shaobo
Issue Date: 20-May-2021
Publisher: Nature Research
Citation: Zhu, Ruichao, Qiu, Tianshuo, Wang, Jiafu, Sui, Sai, Hao, Chenglong, Liu, Tonghao, Li, Yongfeng, Feng, Mingde, Zhang, Anxue, Qiu, Cheng-Wei, Qu, Shaobo (2021-05-20). Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning. Nature Communications 12 (1) : 2974. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Metasurfaces have provided unprecedented freedom for manipulating electromagnetic waves. In metasurface design, massive meta-atoms have to be optimized to produce the desired phase profiles, which is time-consuming and sometimes prohibitive. In this paper, we propose a fast accurate inverse method of designing functional metasurfaces based on transfer learning, which can generate metasurface patterns monolithically from input phase profiles for specific functions. A transfer learning network based on GoogLeNet-Inception-V3 can predict the phases of 28×8 meta-atoms with an accuracy of around 90%. This method is validated via functional metasurface design using the trained network. Metasurface patterns are generated monolithically for achieving two typical functionals, 2D focusing and abnormal reflection. Both simulation and experiment verify the high design accuracy. This method provides an inverse design paradigm for fast functional metasurface design, and can be readily used to establish a meta-atom library with full phase span. © 2021, The Author(s).
Source Title: Nature Communications
ISSN: 2041-1723
DOI: 10.1038/s41467-021-23087-y
Rights: Attribution 4.0 International
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