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Deep learning approach for inverse design of metasurfaces with a wider shape gamut

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Abstract

While the large design degrees of freedom (DOFs) give metasurfaces a tremendous versatility, they make the inverse design challenging. Metasurface designers mostly rely on simple shapes and ordered placements, which restricts the achievable performance. We report a deep learning based inverse design flow that enables a fuller exploitation of the meta-atom shape. Using a polygonal shape encoding that covers a broad gamut of lithographically realizable resonators, we demonstrate the inverse design of color filters in an amorphous silicon material platform. The inverse-designed transmission-mode color filter metasurfaces are experimentally realized and exhibit enhancement in the color gamut.

© 2022 Optica Publishing Group

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Supplementary Material (1)

NameDescription
Supplement 1       Supporting document

Data availability

The source code for the implementation, datasets and saved models can be found in Ref. [35].

35. Soumyashree S. Panda, “A deep learning approach for inverse design of metasurfaces with a wider shape gamut,” GitHub (2022), https://github.com/soumyashree-panda/metasurface-knowledge-discovery

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