  Metasurfaces is an emerging field that enables the manipulation of light by
an ultra-thin structure composed of sub-wavelength antennae and fulfills an
important requirement for miniaturized optical elements. Finding a new design
for a metasurface or optimizing an existing design for a desired functionality
is a computationally expensive and time consuming process as it is based on an
iterative process of trial and error. We propose a deep learning (DL)
architecture dubbed bidirectional autoencoder for nanophotonic metasurface
design via a template search methodology. In contrast with the earlier
approaches based on DL, our methodology addresses optimization in the space of
multiple metasurface topologies instead of just one, in order to tackle the one
to many mapping problem of inverse design. We demonstrate the creation of a
Geometry and Parameter Space Library (GPSL) of metasurface designs with their
corresponding optical response using our DL model. This GPSL acts as a
universal design and response space for the optimization. As an example
application, we use our methodology to design a multi-band gap-plasmon based
half-wave plate metasurface. Through this example, we demonstrate the power of
our technique in addressing the non-uniqueness problem of common inverse
design. Our network converges aptly to multiple metasurface topologies for the
desired optical response with a low mean absolute error between desired optical
response and the optical response of topologies searched. Our proposed
technique would enable fast and accurate design and optimization of various
kinds of metasurfaces with different functionalities.
