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13 March 2024 Inverse design of two-dimensional photonic crystals through physics-informed deep learning
Georgios Katsikas, Vittorio Peano, Florian Marquardt, Ewold Verhagen
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Proceedings Volume PC12896, Photonic and Phononic Properties of Engineered Nanostructures XIV; PC128960M (2024) https://doi.org/10.1117/12.3000747
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
We study the use of deep neural networks towards the prediction of the optical properties of two-dimensional photonic crystals, as well as their inverse design. We incorporate a rigorous tight-binding model as a known operator in the machine learning algorithm. This physics-informed approach allows the prediction of meaningful model parameters rather than the high-dimensional full response, allowing for an efficient method as well as potential insight in the physical workings of specific designs. We demonstrate a four-order-of-magnitude speedup of prediction of bandstructures and field symmetries over full-field calculations, and proof-of-concept inverse design of photonic crystals with large gaps, flat bands, and Dirac-point degeneracies.
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Georgios Katsikas, Vittorio Peano, Florian Marquardt, and Ewold Verhagen "Inverse design of two-dimensional photonic crystals through physics-informed deep learning", Proc. SPIE PC12896, Photonic and Phononic Properties of Engineered Nanostructures XIV, PC128960M (13 March 2024); https://doi.org/10.1117/12.3000747
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KEYWORDS
Photonic crystals
Design and modelling
Deep learning
Crystals
Diffraction
Dispersion
Error analysis
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