  Deep Learning has been a critical part of designing inverse design methods
that are computationally efficient and accurate. An example of this is the
design of photonic metasurfaces by using their photoluminescent spectrum as the
input data to predict their topology. One fundamental challenge of these
systems is their ability to represent nonlinear relationships between sets of
data that have different dimensionalities. Existing design methods often
implement a conditional Generative Adversarial Network in order to solve this
problem, but in many cases the solution is unable to generate structures that
provide multiple peaks when validated. It is demonstrated that in response to
the target spectrum, the Bidirectional Adversarial Autoencoder is able to
generate structures that provide multiple peaks on several occasions. As a
result the proposed model represents an important advance towards the
generation of nonlinear photonic metasurfaces that can be used in advanced
metasurface design.
