【摘 要】
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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 t
【机 构】
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AdvancedSemiconductorLaboratory,KingAbdullahUniversityofScienceandTechnology(KAUST),Thuwal23955-6900
【出 处】
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PhotonicsResearch
论文部分内容阅读
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.
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