Spectrum prediction and inverse design of metasurfaces via transfer learning based on material similarity.

Journal: Optics letters
Published Date:

Abstract

Spectrum prediction and inverse design of metasurfaces based on deep learning have been a hot research topic. The dependence of deep learning on data is a major challenge for its widespread application in the field of metasurfaces. In this letter, we proposed a transfer learning method based on material similarity to accomplish spectrum prediction and the inverse design of metasurfaces. As a proof-of-concept, we investigated the transfer tasks of two types of metasurface, i.e., absorption metasurface and polarization conversion metasurface, whose material properties could be represented by the Drude model to reflect the material similarity, and accomplished the spectrum prediction and inverse design through transfer learning. We achieved 50% data saving, demonstrating reduction of the reliance on training data volume while ensuring network performance. The proposed concept may provide a new avenue for metasurface and metamaterial designs.

Authors

  • Dongchun Wang
  • Hongping Zhou
    Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
  • Zhongyi Guo
  • Kai Guo
    Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND 58202, USA. kai.guo@med.und.edu.

Keywords

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