Metasurfaces offer a flexible framework for the manipulation of light
properties in the realm of thin film optics. Specifically, the polarization of
light can be effectively controlled through the use of thin phase plates. This
study aims to introduce a surrogate optimization framework for these devices.
The framework is applied to develop two kinds of vortex phase masks (VPMs)
tailored for application in astronomical high-contrast imaging. Computational
intelligence techniques are exploited to optimize the geometric features of
these devices. The large design space and computational limitations necessitate
the use of surrogate models like partial least squares Kriging, radial basis
functions, or neural networks. However, we demonstrate the inadequacy of these
methods in modeling the performance of VPMs. To address the shortcomings of
these methods, a data-efficient evolutionary optimization setup using a deep
neural network as a highly accurate and efficient surrogate model is proposed.
The optimization process in this study employs a robust particle swarm
evolutionary optimization scheme, which operates on explicit geometric
parameters of the photonic device. Through this approach, optimal designs are
developed for two design candidates. In the most complex case, evolutionary
optimization enables optimization of the design that would otherwise be
impractical (requiring too much simulations). In both cases, the surrogate
model improves the reliability and efficiency of the procedure, effectively
reducing the required number of simulations by up to 75% compared to
conventional optimization techniques