Inverse design of bright, dielectric metasurfaces color filters based on back-propagation and multi-valued artificial neural networks

Arthur Clini de Souza; Stéphane Lanteri; Hugo Enrique Hernandez-Figueroa; Marco Abbarchi; David Grosso; Badre Kerzabi; Mahmoud Elsawy

doi:10.1117/12.3016617

18 June 2024 Inverse design of bright, dielectric metasurfaces color filters based on back-propagation and multi-valued artificial neural networks

Arthur Clini de Souza, Stéphane Lanteri, Hugo Enrique Hernandez-Figueroa, Marco Abbarchi, David Grosso, Badre Kerzabi, Mahmoud Elsawy

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Proceedings Volume 13017, Machine Learning in Photonics; 130170E (2024) https://doi.org/10.1117/12.3016617
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

The present work showcases an innovative optimization methodology based on deep learning that combines Multi- Valued Artificial Neural Networks and back-propagation optimization. The methodology addresses the inherent limitations of conventional approaches when employed in isolation. We applied the proposed methodology to design structural color filters that surpasses the sRGB gamut while preserving fabrication constraints.

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Arthur Clini de Souza, Stéphane Lanteri, Hugo Enrique Hernandez-Figueroa, Marco Abbarchi, David Grosso, Badre Kerzabi, and Mahmoud Elsawy "Inverse design of bright, dielectric metasurfaces color filters based on back-propagation and multi-valued artificial neural networks", Proc. SPIE 13017, Machine Learning in Photonics, 130170E (18 June 2024); https://doi.org/10.1117/12.3016617

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