  We show that deep generative neural networks, based on global topology
optimization networks (GLOnets), can be configured to perform the
multi-objective and categorical global optimization of photonic devices. A
residual network scheme enables GLOnets to evolve from a deep architecture,
which is required to properly search the full design space early in the
optimization process, to a shallow network that generates a narrow distribution
of globally optimal devices. As a proof-of-concept demonstration, we adapt our
method to design thin film stacks consisting of multiple material types.
Benchmarks with known globally-optimized anti-reflection structures indicate
that GLOnets can find the global optimum with orders of magnitude faster speeds
compared to conventional algorithms. We also demonstrate the utility of our
method in complex design tasks with its application to incandescent light
filters. These results indicate that advanced concepts in deep learning can
push the capabilities of inverse design algorithms for photonics.
