Communication Dans Un Congrès Année : 2024

Cost-efficient deep learning method for mass inverse design of photonic devices

Résumé

We apply an efficient inverse design methodology to conceive metasurface-based beam deflectors. We start by creating a small dataset composed by the results of simple simulations to train two deep neural networks. Once the models are trained, various beam deflectors can be inverse designed without further full-wave simulations. Our approach combines physical intuition about blazed gratings with a state-of-theart optimization methodology, achieving high efficiencies while requiring modest computational effort. One design was successfully fabricated using sub-20 nm thermal scanning probe lithography.
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hal-04907835 , version 1 (23-01-2025)

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Arthur Clini de Souza, Paloma Elias da Silva Pellegrini, Silvia Vaz Guerra Nista, Stéphane Lanteri, Hugo Enrique Hernandez-Figueroa, et al.. Cost-efficient deep learning method for mass inverse design of photonic devices. SBFoton IOPC 2024 - SBFoton International Optics and Photonics Conference, Nov 2024, Salvador, Brazil. pp.3, ⟨10.1109/SBFotonIOPC62248.2024.10813471⟩. ⟨hal-04907835⟩
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