  Over the past decade, artificially engineered optical materials and
nanostructured thin films have revolutionized the area of photonics by
employing novel concepts of metamaterials and metasurfaces where spatially
varying structures yield tailorable, "by design" effective electromagnetic
properties. The current state-of-the-art approach to designing and optimizing
such structures relies heavily on simplistic, intuitive shapes for their unit
cells or meta-atoms. Such approach can not provide the global solution to a
complex optimization problem where both meta-atoms shape, in-plane geometry,
out-of-plane architecture, and constituent materials have to be properly chosen
to yield the maximum performance. In this work, we present a novel
machine-learning-assisted global optimization framework for photonic
meta-devices design. We demonstrate that using an adversarial autoencoder
coupled with a metaheuristic optimization framework significantly enhances the
optimization search efficiency of the meta-devices configurations with complex
topologies. We showcase the concept of physics-driven compressed design space
engineering that introduces advanced regularization into the compressed space
of adversarial autoencoder based on the optical responses of the devices.
Beyond the significant advancement of the global optimization schemes, our
approach can assist in gaining comprehensive design "intuition" by revealing
the underlying physics of the optical performance of meta-devices with complex
topologies and material compositions.
