Inverse Design of Free-Form Metasurfaces with Deep Neural Networks
Paper in proceeding, 2022
We show that the free-form inverse design of nanophotonic matasurfaces can be solved with a modified CGAN machine learning method that balances the accuracy of desired optical properties with experimental feasibility.
Author
Timo Gahlmann
Chalmers, Physics, Condensed Matter and Materials Theory
Philippe Tassin
Chalmers, Physics, Condensed Matter and Materials Theory
Optics InfoBase Conference Papers
21622701 (eISSN)
FM5H.79781557528209 (ISBN)
2022 Conference on Lasers and Electro-Optics, CLEO 2022
San Jose, USA,
San Jose, USA,
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Language Technology (Computational Linguistics)
Aerospace Engineering
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