Multifunctional Metasurface Design with a Generative Adversarial Network

Metasurfaces have enabled precise electromagnetic (EM) 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 EM responses), which makes the design of multifunctional metasurfaces a key challenge in this field. In this paper, a generative adversarial network that can tackle this problem and generate meta-atom/metasurface designs to meet multifunctional design goals is presented. 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 EM responses but different physical features can provide added latitude to accommodate other considerations such as fabrication constraints and tolerances. The network's ability to produce a variety of multifunctional metasurface designs is demonstrated by presenting a bifocal metalens, a polarization-multiplexed beam deflector, a polarization-multiplexed metalens, and a polarization-independent metalens.

Automated summary

This paper presents a generative adversarial network that can generate meta-atom/metasurface designs to meet multifunctional design goals. This new methodology is efficient and flexible, suitable for multifunctional device design, and able to produce different classes of structures to accommodate other considerations.