  Designing complex physical systems, including photonic structures, is
typically a tedious trial-and-error process that requires extensive simulations
with iterative sweeps in multi-dimensional parameter space. To circumvent this
conventional approach and substantially expedite the discovery and development
of photonic structures, here we develop a framework leveraging both a deep
generative model and a modified evolution strategy to automate the inverse
design of engineered nanophotonic materials. The capacity of the proposed
methodology is tested through the application to a case study, where
metasurfaces in either continuous or discrete topologies are generated in
response to customer-defined spectra at the input. Through a variational
autoencoder, all potential patterns of unit nanostructures are encoded into a
continuous latent space. An evolution strategy is applied to vectors in the
latent space to identify an optimized vector whose nanostructure pattern
fulfills the design objective. The evaluation shows that over 95% accuracy can
be achieved for all the unit patterns of the nanostructure tested. Our scheme
requires no prior knowledge of the geometry of the nanostructure, and, in
principle, allows joint optimization of the dimensional parameters. As such,
our work represents an efficient, on-demand, and automated approach for the
inverse design of photonic structures with subwavelength features.
