Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning.
Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning.
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通过迁移学习快速实现功能超表面的相模式逆向设计范例。
DOI：
10.1038/s41467-021-23087-y
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发表时间：
2021-05-20
影响因子：
16.6
通讯作者：
Qu S
中科院分区：
文献类型：
--
作者：
Zhu R;Qiu T;Wang J;Sui S;Hao C;Liu T;Li Y;Feng M;Zhang A;Qiu CW;Qu S
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. The design and optimization of a metasurface is a computationally- and time-consuming effort. Here, the authors propose a neural network-based algorithm for functional metasurface design, and demonstrate it for some functional metasurfaces.
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