  Optical computing is an emerging technology for next-generation efficient
artificial intelligence (AI) due to its ultra-high speed and efficiency.
Electromagnetic field simulation is critical to the design, optimization, and
validation of photonic devices and circuits. However, costly numerical
simulation significantly hinders the scalability and turn-around time in the
photonic circuit design loop. Recently, physics-informed neural networks have
been proposed to predict the optical field solution of a single instance of a
partial differential equation (PDE) with predefined parameters. Their
complicated PDE formulation and lack of efficient parametrization mechanisms
limit their flexibility and generalization in practical simulation scenarios.
In this work, for the first time, a physics-agnostic neural operator-based
framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain
Maxwell PDEs for ultra-fast parametric photonic device simulation. We balance
the efficiency and generalization of NeurOLight via several novel techniques.
Specifically, we discretize different devices into a unified domain, represent
parametric PDEs with a compact wave prior, and encode the incident light via
masked source modeling. We design our model with parameter-efficient
cross-shaped NeurOLight blocks and adopt superposition-based augmentation for
data-efficient learning. With these synergistic approaches, NeurOLight
generalizes to a large space of unseen simulation settings, demonstrates
2-orders-of-magnitude faster simulation speed than numerical solvers, and
outperforms prior neural network models by ~54% lower prediction error with
~44% fewer parameters. Our code is available at
https://github.com/JeremieMelo/NeurOLight.
