Barre 22-32 – 407
Mahmoud Elsawy
Mahmoud Elsawy is an experienced researcher with a strong background in electromagnetic modeling for linear and nonlinear photonic devices. He received his PhD from Aix-Marseille University, France, in 2017, specializing in optics, photonics, and image processing. His PhD thesis focused on the development of computational models for the design of realistic integrated nonlinear plasmonic waveguides. He spent a year as a postdoctoral researcher at the Institut Fresnel in Marseille, France. After another postdoc at Inria Sophia Antipolis, France, in the field of numerical optimization of nanophotonics, he became a permanent member of the Atlantis project team in December 2020 through an Inria Starting Faculty Position (ISFP). His research activities focus on the modeling and design of innovative passive and programmable metasurface devices.
Abstract
In this presentation, I am excited to share with you our cutting-edge optimization approach for multiobjective metasurface setups. Our method is based on statistical learning, which utilizes surrogate modeling to predict the behavior of new designs during the optimization process [1-2]. This approach allows us to rapidly converge to the global set of optimal solutions while reducing the number of solver calls and iterations compared to traditional global evolutionary strategies.
We put our optimization approach to the test by optimizing a 3D achromatic metalens with a numerical aperture greater than 0.5. Our results were remarkable, achieving focusing efficiencies of almost 50% for three colors (red, green, blue). This achievement sets a new record for RGB metalens of this kind, and it was accomplished using simple cylindrical nanopillar geometries that are easier to fabricate than complex freeform geometries [3]. Additionally, we developed a adopted our novel optimization method to consider the fabrication imperfections relying on the metamodel context [4].
I will discuss as well our latest contributions [5], to make use of a sophisticated deep neural network structure to optimize and simulate vivid structural color filter metasurfaces.
In the second part of my presentation, I will discuss our innovative design strategy to achieve full phase modulation of light reflected from an arbitrary active metasurface with near-unity efficiency [4]. We adopted our advanced optimization method and considered the near-field coupling between strongly resonant pixels and the nonlocal response to maximize the active beam steering performance. This breakthrough technology has exciting applications in imaging microscopy, high-resolution image projection, optical communication, and 3D light detection and ranging (LiDAR).
Overall, our cutting-edge optimization approach together with our innovative design strategies represent significant breakthroughs in the field of metasurface setups, with the potential to transform a wide range of applications.
[1] M. Elsawy, et al, «Global optimization of metasurface designs using statistical learning methods», Scientific Reports, Vol. 9, No. 17918, (2019).
[2] E. Isnard, S. Héron, S. Lanteri, and M. Elsawy, “Advancing wavefront shaping with resonant nonlocal metasurfaces: beyond the limitations of lookup tables”. Sci Rep 14, 1555 (2024) .
[3] M. Elsawy, et. al, Multiobjective statistical learning optimization of RGB metalens, ACS Photonics, Vol. 8,No. 8, 2498–2508 (2021).
[4] M. Elsawy, et. al, Optimization of metasurfaces under geometrical uncertainty using statistical learning, Optics Express 29(19), 29887–29898 (2021).
[5] A. Clini de Souza, S. Lanteri, and M. Elsawy. et al, “Back-propagation optimization and multi-valued artificial neural networks for highly vivid structural color filter metasurfaces”, Sci Rep 13, 21352 (2023).
[6] M. Elsawy, et. al, Universal Active Metasurfaces for Ultimate Wavefront Molding by Manipulating the Reflection Singularities. Laser Photonics Rev, 2200880, (2023)
