Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Multiobjective statistical learning optimization of RGB metalens

Abstract : Modelling of multi-wavelength metasurfaces relies on adjusting the phase of indi-vidual nanoresonators at several wavelengths.The traditional procedure neglects thenear-field coupling between the nanoresonators, which dramatically reduces the over-all diffraction efficiency, bandwidth, numerical aperture and device diameter.Anotheralternative design strategy is to combine a numerical optimization technique with full-wave simulations to mitigate this problem and optimize the entire metasurface at once.Here, we present a global multiobjective optimization technique that utilizes statisticallearning method to optimize RGB spherical metalenses at the visible wavelengths. Theoptimization procedure, coupled to a high-order full-wave solver, accounts for the nearfield coupling between the resonators. High numerical aperture RGB lenses(NA= 0.47and NA= 0.56) of 8μm and 10μm diameters are optimized with numerical average1 focusing efficiencies of 55% and 45%, with an average focusing error smaller than 6%for the RGB colors. The fabricated and experimentally characterized devices present44.16% and 31.5% respective efficiencies. The reported performances represent thehighest focusing efficiencies for highNA >0.5 RGB metalenses obtained so far. Theintegration of multi-wavelength metasurfaces in portable and wearable electronic de-vices requires high performances to offer a variety of applications ranging from classicalimaging to virtual and augmented reality.
Complete list of metadata
Contributor : Mahmoud Elsawy <>
Submitted on : Tuesday, May 25, 2021 - 10:49:57 AM
Last modification on : Monday, June 14, 2021 - 10:24:57 AM


Files produced by the author(s)


  • HAL Id : hal-03212349, version 2


Mahmoud Elsawy, Anthony Gourdin, Mickaël Binois, Régis Duvigneau, Didier Felbacq, et al.. Multiobjective statistical learning optimization of RGB metalens. 2021. ⟨hal-03212349v2⟩



Record views


Files downloads