We demonstrate a neural network capable of designing on-demand multiple symmetry-protected bound states in the continuum(BICs)in freeform structures with predefined symmetry.The latent representation of the freeform s...We demonstrate a neural network capable of designing on-demand multiple symmetry-protected bound states in the continuum(BICs)in freeform structures with predefined symmetry.The latent representation of the freeform structures allows the tuning of the geometry in a differentiable,continuous way.We show the rich band inversion and accidental degeneracy in these freeform structures by interacting with the latent representation directly.Moreover,a high design accuracy is demonstrated for arbitrary control of multiple BIC frequencies by using a photonic property readout network to interpret the latent representation.展开更多
基金King Abdullah University of Science and Technology Baseline Fund(BAS/1/1664-01-01)Competitive Research Grants(URF/1/3437-01-01,URF/1/3771-01-01).
文摘We demonstrate a neural network capable of designing on-demand multiple symmetry-protected bound states in the continuum(BICs)in freeform structures with predefined symmetry.The latent representation of the freeform structures allows the tuning of the geometry in a differentiable,continuous way.We show the rich band inversion and accidental degeneracy in these freeform structures by interacting with the latent representation directly.Moreover,a high design accuracy is demonstrated for arbitrary control of multiple BIC frequencies by using a photonic property readout network to interpret the latent representation.
