  Metasurfaces have become a promising means for manipulating optical
wavefronts in flat and high-performance optical devices. Conventional
metasurface device design relies on trial-and-error methods to obtain target
electromagnetic (EM) response, an approach that demands significant efforts to
investigate the enormous number of possible meta-atom structures. In this
paper, a deep neural network approach is introduced that significantly improves
on both speed and accuracy compared to techniques currently used to assemble
metasurface-based devices. Our neural network approach overcomes three key
challenges that have limited previous neural-network-based design schemes:
input/output vector dimensional mismatch, accurate EM-wave phase prediction, as
well as adaptation to 3-D dielectric structures, and can be generically applied
to a wide variety of metasurface device designs across the entire
electromagnetic spectrum. Using this new methodology, examples of neural
networks capable of producing on-demand designs for meta-atoms, metasurface
filters, and phase-change reconfigurable metasurfaces are demonstrated.
