Inverse Design of Free-Form Metasurfaces with Deep Neural Networks

Timo Gahlmann; Philippe Tassin

doi:10.1364/CLEO_QELS.2022.FM5H.7

Conference on Lasers and Electro-Optics
Technical Digest Series (Optica Publishing Group, 2022),
paper FM5H.7
•https://doi.org/10.1364/CLEO_QELS.2022.FM5H.7

Inverse Design of Free-Form Metasurfaces with Deep Neural Networks

Timo Gahlmann and Philippe Tassin

CLEO: QELS_Fundamental Science 2022

San Jose, California United States
15–20 May 2022
ISBN: 978-1-957171-05-0

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Computational Design (FM5H)

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T. Gahlmann and P. Tassin, "Inverse Design of Free-Form Metasurfaces with Deep Neural Networks," in Conference on Lasers and Electro-Optics, Technical Digest Series (Optica Publishing Group, 2022), paper FM5H.7.

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Abstract

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.

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