影响因子：2.6

DOI码：10.3390/electronics14061164

所属单位：西北大学电子信息学院

教研室：电子科学与技术系

发表刊物：Electronics

关键字：linear-to-circular polarization converter; machine learning guided; graphene based; vanadium dioxide; axial ratio optimization

摘要：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.

论文类型：期刊论文

学科门类：工学

一级学科：电子科学与技术

文献类型：J

卷号：6

期号：14

页面范围：1164

是否译文：否

发表时间：2025-03-16

收录刊物：SCI

第一作者：Yilin Xie

通讯作者：Cheng Chen*

合写作者：Jia Liu

合写作者：Zhihao Li

合写作者：Shilei Tian

合写作者：Jixin Wang

合写作者：Wu Zhao

合写作者：Johan Stiens