  The advent of two-dimensional metamaterials in recent years has ushered in a
revolutionary means to manipulate the behavior of light on the nanoscale. The
effective parameters of these architected materials render unprecedented
control over the optical properties of light, thereby eliciting previously
unattainable applications in flat lenses, holographic imaging, and emission
control among others. The design of such structures, to date, has relied on the
expertise of an optical scientist to guide a progression of electromagnetic
simulations that iteratively solve Maxwell's equations until a locally
optimized solution can be attained. In this work, we identify a solution to
circumvent this intuition-guided design by means of a deep learning
architecture. When fed an input set of optical spectra, the constructed
generative network assimilates a candidate pattern from a user-defined dataset
of geometric structures in order to match the input spectra. The generated
metamaterial patterns demonstrate high fidelity, yielding equivalent optical
spectra at an average accuracy of about 0.9. This approach reveals an
opportunity to expedite the discovery and design of metasurfaces for tailored
optical responses in a systematic, inverse-design manner.
