Global Optimization and Deep Learning Techniques For Freeform Nanophotonic Metadevices

Engineered freeform components are advancing the state of the art in the field of nanophotonics. To this end, optical metasurfaces are increasingly desirable due to their ability to control the phase, magnitude, and polarization of transmitted (and/or reflected) light and improvements in nanofabrication techniques. While metasurfaces have demonstrated unprecedented control over optical performances, it is not well understood how to determine the geometries necessary to achieve a targeted optical performance. That being so, optimization and inverse-design techniques have proven invaluable to engineers seeking to realize metadevices with specific targeted functionalities. In this presentation, we discuss the advantages of our metadevice design process which utilizes a combination of global optimization and deep learning to realize an accelerated mutliobjective framework for generating highly performant designs that are robust to fabrication uncertainties.