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

TL;DR

WaveY-Net is a physics-augmented deep learning model that rapidly predicts electromagnetic fields in photonic structures, combining neural networks with Maxwell's equations for high accuracy and speed in device optimization.

Contribution

This work introduces WaveY-Net, a novel hybrid neural network that integrates physical laws into deep learning for efficient electromagnetic simulation of photonic devices.

Findings

Achieves ultra-fast electromagnetic field predictions with high accuracy.

Effectively used in the optimization of silicon nanostructure arrays.

Demonstrates potential to replace traditional Maxwell simulators in photonics design.

Abstract

The calculation of electromagnetic field distributions within structured media is central to the optimization and validation of photonic devices. 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. We anticipate that physics-augmented networks will serve as a viable Maxwell simulator replacement for many classes of photonic systems, transforming the way they are designed.