  Designing photonic structures requires electromagnetic simulations, which
often require high computational costs. Researchers have developed surrogate
solvers for predicting electric fields to alleviate the computational issues.
However, existing surrogate solvers are limited to performing inference at
fixed simulation conditions and require retraining for different conditions. To
address this, we propose Wave Interpolation Neural Operator (WINO), a novel
surrogate solver enabling simulation condition interpolation across a
continuous spectrum of broadband wavelengths. WINO introduces the Fourier Group
Convolution Shuffling operator and a new conditioning method to efficiently
predict electric fields from both trained and untrained wavelength data,
achieving significant improvements in parameter efficiency and spectral
interpolation performance. Our model demonstrates approximately 100 times
faster performance than traditional finite-difference frequency-domain
simulations. Moreover, compared to the state-of-the-art model, we achieve a 74%
reduction in parameters and 80.5% improvements in prediction accuracy for
untrained wavelengths, and 13.2% improvements for trained wavelengths.
