Optimization of metasurfaces under geometrical uncertainty using statistical learning

The performance of metasurfaces measured experimentally often discords with expected values from numerical optimization. These discrepancies are attributed to the poor tolerance of metasurface building blocks with respect to fabrication uncertainties and nanoscale imperfections. Quantifying their efficiency drop according to geometry variation are crucial to improve the range of application of this technology. Here, we present a novel optimization methodology to account for the manufacturing errors related to metasurface designs. In this approach, accurate results using probabilistic surrogate models are used to reduce the number of costly numerical simulations. We employ our procedure to optimize the classical beam steering metasurface made of cylindrical nanopillars. Our numerical results yield a design that is twice more robust compared to the deterministic case. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

This study introduces a novel optimization method to account for manufacturing errors in metasurface designs, using probabilistic surrogate models to reduce the number of numerical simulations and improve design robustness.