An Efficient Design Method for a Metasurface Polarizer with High Transmittance and Extinction Ratio

Abstract

In fields such as polarization navigation and imaging, there is a demand for polarizers to simultaneously possess a high transmittance and extinction ratio. However, most studies focus solely on a single metric for polarizer design. To address this issue, this paper proposed a metasurface polarizer with a transition layer and its efficient design method, aiming to enhance the polarizers’ transmittance and extinction ratio performance simultaneously. An all-dielectric metasurface polarizer with a TiO2 transition layer was designed, and a tandem neural network was constructed by combining forward prediction and inverse design networks. The network is successfully trained by utilizing structural parameters and spectral response Tx and Ty datasets collected through simulation software. A high-performance metasurface polarizer was designed under the desired metrics with a transmittance of 90% and an extinction ratio of 40 dB. The proposed method in this paper made progress in the comprehensive optimization of polarizer performance. It is more accurate and efficient compared to other design methods and is better suited for practical applications.

Keywords

• deep learning
• inverse design
• metasurface
• polarizer