  Metasurfaces are subwavelength-structured artificial media that can shape and
localize electromagnetic waves in unique ways. The inverse design of these
devices is a non-convex optimization problem in a high dimensional space,
making global optimization a major challenge. We present a new type of
population-based global optimization algorithm for metasurfaces that is enabled
by the training of a generative neural network. The loss function used for
backpropagation depends on the generated pattern layouts, their efficiencies,
and efficiency gradients, which are calculated by the adjoint variables method
using forward and adjoint electromagnetic simulations. We observe that the
distribution of devices generated by the network continuously shifts towards
high performance design space regions over the course of optimization. Upon
training completion, the best generated devices have efficiencies comparable to
or exceeding the best devices designed using standard topology optimization.
Our proposed global optimization algorithm can generally apply to other
gradient-based optimization problems in optics, mechanics and electronics.
