Papers
arxiv:2610.11500

Learning qBIC Resonances across Metasurface Families in Dielectric Fourier Space

Published on Oct 8
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Abstract

Dielectric metasurface families are mapped to a shared reciprocal-lattice grid using frozen Fourier channels and a dual-branch neural architecture to predict broadband spectra and ultranarrow qBIC resonances with high accuracy, enabling cross-geometry comparison and inverse design.

Bound states in the continuum (BIC) metasurfaces are typically described by geometry-specific parameters, hindering cross-geometry comparison, while ultranarrow qBIC features are easily diluted in full-spectrum learning. Here, 2015 samples from seven dielectric metasurface families are mapped to a shared reciprocal-lattice grid, where two frozen low-order Fourier channels capture resonance shifts with mean within-branch R^2 values of 0.871-0.999. Field-level analysis of two representative branches further confirms that these shifts are consistent with the Maxwell-Fourier perturbation picture. A five-channel K-space backbone models the broadband spectrum, while a local complex K-space expert parameterizes the qBIC resonance through a differentiable Fano layer. The expert reduces resonance-position mean absolute error (MAE) from 3.2 to 0.95 nm and the resonance-depth error by 14-fold on a geometry-blocked test set. The same coordinate supports spectrum-to-structure reconstruction.

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