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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.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Robert Lupoiu,Chenkai Mao,Yixuan Shao,Mingkun Chen, andJonathan 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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Robert Lupoiu, Chenkai Mao, Yixuan Shao, Mingkun Chen, 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