WaveY-Net: physics-augmented deep-learning for high-speed electromagnetic simulation and optimization

Mingkun Chen; Robert Lupoiu; Chenkai Mao; Der-Han Huang; Jiaqi Jiang; Philippe Lalanne; Jonathan A. Fan

doi:10.1117/12.2612418

5 March 2022 WaveY-Net: physics-augmented deep-learning for high-speed electromagnetic simulation and optimization

Mingkun Chen, Robert Lupoiu, Chenkai Mao, Der-Han Huang, Jiaqi Jiang, Philippe Lalanne, Jonathan A. Fan

Proceedings Volume 12011, High Contrast Metastructures XI; 120110C (2022) https://doi.org/10.1117/12.2612418
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

We introduce WaveY-Net, a hybrid data- and physics-augmented convolutional neural network that can predict electromagnetic field distributions with ultra fast speeds and high accuracy for entire classes of dielectric photonic structures. This accuracy is achieved by training the neural network to learn only the magnetic near-field distributions of a system and to use a discrete formalism of Maxwell's equations in two ways: as physical constraints in the loss function and as a means to calculate the electric fields from the magnetic fields. As a model system, we construct a surrogate simulator for periodic silicon nanostructure arrays and show that the high speed simulator can be directly and effectively used in the local and global freeform optimization of metagratings.

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Citation Download Citation

Mingkun Chen, Robert Lupoiu, Chenkai Mao, Der-Han Huang, Jiaqi Jiang, Philippe Lalanne, and Jonathan A. Fan "WaveY-Net: physics-augmented deep-learning for high-speed electromagnetic simulation and optimization", Proc. SPIE 12011, High Contrast Metastructures XI, 120110C (5 March 2022); https://doi.org/10.1117/12.2612418

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