Inverse design of polarization conversion metasurfaces by deep neural networks.

To address the problem of multiple solutions and improve the calculating speed, we construct a tandem architecture consisting of a forward modeling network and an inverse design network. Using this combined network, we inversely design the circular polarization converter and analyze the effect of different design parameters on the prediction accuracy of the polarization conversion rate. The average mean square error of the circular polarization converter is 0.