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Bayesian optimization and rigorous modelling of a highly efficient 3D metamaterial mode converter

Abstract : We combine a statistical learning-based global optimization strategy with a high order 3D Discontinuous Galerkin Time-Domain (DGTD) solver to design a compact and highly efficient graded index photonic metalens. The metalens is composed of silicon (Si) strips of varying widths (in the transverse direction) and lengths (in the propagation direction) and operates at the telecommunication wavelength. In our work, we tackle the challenging Transverse Electric case (TE) where the incident electric field is polarized perpendicular to strips direction. We reveal that the focusing efficiency approaches 80% for the traditional design with fixed strip lengths and varying widths. Nevertheless, we demonstrate numerically that the efficiency is as high as 87% for a design with varying strip lengths along the propagation direction.
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https://www.hal.inserm.fr/inserm-02473373
Contributor : Mahmoud Elsawy <>
Submitted on : Tuesday, December 15, 2020 - 8:10:44 PM
Last modification on : Wednesday, December 16, 2020 - 4:03:57 AM

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Mahmoud M. R. Elsawy, Karim Hassan, Salim Boutami, Stephane Lanteri. Bayesian optimization and rigorous modelling of a highly efficient 3D metamaterial mode converter. OSA Continuum, OSA Publishing, 2020, ⟨10.1364/OSAC.393220⟩. ⟨inserm-02473373v2⟩

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