Wavelength-dependent responses and machine learning in nanophotonics modeling

Francesco Ferranti

doi:10.1117/12.3025138

18 June 2024 Wavelength-dependent responses and machine learning in nanophotonics modeling

Francesco Ferranti

Proceedings Volume 13017, Machine Learning in Photonics; 130170D (2024) https://doi.org/10.1117/12.3025138
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Machine learning techniques have been proposed in the literature for the modeling of photonic devices. These techniques can be used to speed up the design process. The data samples needed to build machine learning models are collected from electromagnetic simulations. Electromagnetic solvers can result computationally expensive and therefore minimizing the computational effort needed to collect these data samples is an important aspect. Using frequency-domain electromagnetic solvers to collect data samples requires a suitable sampling of the wavelength variable to avoid undersampling and oversampling phenomena. An adaptive frequency-domain sampling approach for nanophotonic applications is illustrated in this work.

Citation Download Citation

Francesco Ferranti "Wavelength-dependent responses and machine learning in nanophotonics modeling", Proc. SPIE 13017, Machine Learning in Photonics, 130170D (18 June 2024); https://doi.org/10.1117/12.3025138

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