Deep Learning and Optimization for Efficient and Robust Nanophotonic Design

Nanophotonic device design requires efficient full-wave solvers and optimization techniques. To this end, deep learning shows promise for improving both forward- and inverse-design aspects of such problems. Meanwhile, multiobjective and topology optimization approaches have proven successful in realizing highly performant freeform nanophotonic devices with tailored functionalities. Combining deep learning with advanced optimization techniques gives designers beyond state-of-the-art tools for realizing extremely efficient nanophotonic device design cycles.