Realization and Inverse Design of Multifunctional Steerable Transflective Linear-to-Circular Polarization Converter Empowered by Machine Learning

The development of polarization converters is crucial for various applications, such as communication and sensing technologies. However, traditional polarization converters often encounter challenges in optimizing performance due to the complexity of multiparameter structures. In this study, we propose a novel multiparameter linear-to-circular polarization (LCP) converter design that addresses the difficulties of comprehensive optimization, where balancing multiple structural parameters is key to maximizing device performance. To solve this issue, we employ a machine learning (ML)-guided approach that effectively navigates the complexities of parameter interactions and optimizes the design. By utilizing the XGBoost model, we analyze a dataset of over 1.3 million parameter combinations and successfully predict high-performing designs. The results highlight that key parameters, such as the graphene Fermi level, square frame size, and VO2 conductivity, play a dominant role in determining the performance of the LCP converter. This approach not only provides new insights into the design of LCP converters but also offers a practical solution to the complex challenge of multiparameter optimization in device engineering.