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

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 2^8×8 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

SUBMITTER: Zhu R 

PROVIDER: S-EPMC8137937 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature