Multiobjective Statistical Learning Optimization of RGB Metalens
Mahmoud Elsawy, Anthony Gourdin, Mickaël Binois, Régis Duvigneau, Didier Felbacq, Samira Khadir, Patrice Genevet, Stéphane Lanteri
Abstract
Modeling of multiwavelength metasurfaces relies on adjusting the phase of individual nanoresonators at several wavelengths. The traditional procedure neglects the near-field coupling between the nanoresonators, which dramatically reduces the overall diffraction efficiency, bandwidth, numerical aperture, and device diameter. Another alternative 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 a statistical learning method to optimize RGB spherical metalenses at visible wavelengths. The optimization procedure, coupled to a high-order full-wave solver, accounts for the near-field coupling between the resonators. High-numerical-aperture RGB lenses (NA = 0.47 and 0.56) of 8 and 10 μm diameters are optimized with numerical average focusing efficiencies of 55% and 45%, respectively, with an average focusing error of less than 6% for the RGB colors. The fabricated and experimentally characterized devices present 44.16% and 31.5% respective efficiencies. The reported performances represent the highest focusing efficiencies for high NA > 0.5 RGB metalenses obtained so far. The integration of multiwavelength metasurfaces in portable and wearable electronic devices requires high performances to offer a variety of applications ranging from classical imaging to virtual and augmented reality.