  The conventional approach to nanophotonic metasurface design and optimization
for a targeted electromagnetic response involves exploring large geometry and
material spaces, which is computationally costly, time consuming and a highly
iterative process based on trial and error. Moreover, the non-uniqueness of
structural designs and high non-linearity between electromagnetic response and
design makes this problem challenging. To model this non-intuitive relationship
between electromagnetic response and metasurface structural design as a
probability distribution in the design space, we introduce a cyclical deep
learning (DL) based framework for inverse design of nanophotonic metasurfaces.
The proposed framework performs inverse design and optimization mechanism for
the generation of meta-atoms and meta-molecules as metasurface units based on
DL models and genetic algorithm. The framework includes consecutive DL models
that emulate both numerical electromagnetic simulation and iterative processes
of optimization, and generate optimized structural designs while simultaneously
performing forward and inverse design tasks. A selection and evaluation of
generated structural designs is performed by the genetic algorithm to construct
a desired optical response and design space that mimics real world responses.
Importantly, our cyclical generation framework also explores the space of new
metasurface topologies. As an example application of utility of our proposed
architecture, we demonstrate the inverse design of gap-plasmon based half-wave
plate metasurface for user-defined optical response. Our proposed technique can
be easily generalized for designing nanophtonic metasurfaces for a wide range
of targeted optical response.
