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

Panda, Soumyashree S.Soumyashree S.PandaChoudhary, SumitSumitChoudharyJoshi, SiddharthSiddharthJoshiSharma, Satinder K.Satinder K.SharmaHegde, Ravi S.Ravi S.Hegde2025-08-312025-08-312022-05-1510.1364/OL.4587462-s2.0-85130070016https://d8.irins.org/handle/IITG2025/2607835561407While 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.falseDeep learning approach for inverse design of metasurfaces with a wider shape gamutArticle153947942586-258915 May 20228arJournal9