  Metasurfaces have enabled precise electromagnetic wave manipulation with
strong potential to obtain unprecedented functionalities and multifunctional
behavior in flat optical devices. These advantages in precision and
functionality come at the cost of tremendous difficulty in finding individual
meta-atom structures based on specific requirements (commonly formulated in
terms of electromagnetic responses), which makes the design of multifunctional
metasurfaces a key challenge in this field. In this paper, we present a
Generative Adversarial Networks (GAN) that can tackle this problem and generate
meta-atom/metasurface designs to meet multifunctional design goals. Unlike
conventional trial-and-error or iterative optimization design methods, this new
methodology produces on-demand free-form structures involving only a single
design iteration. More importantly, the network structure and the robust
training process are independent of the complexity of design objectives, making
this approach ideal for multifunctional device design. Additionally, the
ability of the network to generate distinct classes of structures with similar
electromagnetic responses but different physical features could provide added
latitude to accommodate other considerations such as fabrication constraints
and tolerances. We demonstrate the network's ability to produce a variety of
multifunctional metasurface designs by presenting a bifocal metalens, a
polarization-multiplexed beam deflector, a polarization-multiplexed metalens
and a polarization-independent metalens.
