Neural Networks enabled Forward and Inverse Design of Reconfigurable Metasurfaces

Nanophotonics has joined the application areas of deep neural networks (DDNs) in recent years. Various network architectures and learning approaches have been employed to design and simulate nanophotonic structures and devices. Design and simulation of reconfigurable metasurfaces is another promising application area for neural network enabled nanophotonic design. The tunable optical response of these metasurfaces rely on the phase transitions of phase-change materials, which correspond to significant changes in their dielectric permittivity. Consequently, simulation and design of these metasurfaces requires the ability to model a diverse span of optical properties. In this work, to realize forward and inverse design of reconfigurable metasurfaces, we construct forward and inverse networks to model a wide range of optical characteristics covering from lossless dielectric to lossy plasmonic materials. As proof-of-concept demonstrations, we design a Ge2Sb2Te5 (GST) tunable resonator and a VO2 tunable absorber using our forward and inverse networks, respectively.