Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning

Ruichao Zhu, Tianshuo Qiu, Jiafu Wang (), Sai Sui (), Chenglong Hao, Tonghao Liu, Yongfeng Li, Mingde Feng, Anxue Zhang, Cheng-Wei Qiu () and Shaobo Qu ()
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Ruichao Zhu: Air Force Engineering University
Tianshuo Qiu: Air Force Engineering University
Jiafu Wang: Air Force Engineering University
Sai Sui: Air Force Engineering University
Chenglong Hao: National University of Singapore
Tonghao Liu: Air Force Engineering University
Yongfeng Li: Air Force Engineering University
Mingde Feng: Air Force Engineering University
Anxue Zhang: Xi’an Jiaotong University
Cheng-Wei Qiu: National University of Singapore
Shaobo Qu: Air Force Engineering University

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-23087-y

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