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13 March 2024 Ultra-fast metalens optimization using conditional physics-augmented deep learning surrogate solvers
Robert Lupoiu, Chenkai Mao, Yixuan Shao, Mingkun Chen, Jonathan A. Fan
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Proceedings Volume PC12897, High Contrast Metastructures XIII; PC128970P (2024) https://doi.org/10.1117/12.3005029
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
Physics-augmented training schemes have enabled the use of ultra-fast deep learning surrogate solvers in adjoint optimization algorithms, accelerating design by many orders of magnitude. However, the utility of these solvers for device design is severely limited by their inability to function outside of fixed simulation parameters. We present a foundational method for conditioning deep learning surrogate solvers on arbitrary parameters, such as the source incidence angle. We then demonstrate the capability of a conditional deep learning model to optimize high-efficiency aperiodic metapixel deflectors that are fashioned to create a large-area metalens.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Robert Lupoiu, Chenkai Mao, Yixuan Shao, Mingkun Chen, and Jonathan A. Fan "Ultra-fast metalens optimization using conditional physics-augmented deep learning surrogate solvers", Proc. SPIE PC12897, High Contrast Metastructures XIII, PC128970P (13 March 2024); https://doi.org/10.1117/12.3005029
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KEYWORDS
Deep learning
Ultrafast phenomena
Dielectrics
Education and training
Machine learning
Head
Mathematical optimization
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