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

Autor(es)

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

• Tipo de documento: Artigo Científico
• Fonte: MACHINE LEARNING IN PHOTONICS; v. 13017, p. 10-pg., 2024-01-01.

Resumo

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. (AU)

• Processo FAPESP: 21/11380-5 - CPTEn - Centro Paulista de Estudos da Transição Energética
• Beneficiário: Luiz Carlos Pereira da Silva
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Ciência para o Desenvolvimento
• Processo FAPESP: 21/06506-0 - Metasurfaces totalmente dielétricas fortemente ressonantes baseadas em modos quase-escuros e toroidais
• Beneficiário: Hugo Enrique Hernández Figueroa
• Modalidade de apoio: Auxílio à Pesquisa - Regular