  A key challenge in metasurface design is the development of algorithms that
can effectively and efficiently produce high performance devices. Design
methods based on iterative optimization can push the performance limits of
metasurfaces, but they require extensive computational resources that limit
their implementation to small numbers of microscale devices. We show that
generative neural networks can train from images of periodic,
topology-optimized metagratings to produce high-efficiency, topologically
complex devices operating over a broad range of deflection angles and
wavelengths. Further iterative optimization of these designs yields devices
with enhanced robustness and efficiencies, and these devices can be utilized as
additional training data for network refinement. In this manner, generative
networks can be trained, with a onetime computation cost, and used as a design
tool to facilitate the production of near-optimal, topologically-complex device
designs. We envision that such data-driven design methodologies can apply to
other physical sciences domains that require the design of functional elements
operating across a wide parameter space.
