Impact Factor2.6

DOI number:10.3390/electronics14061164

Affiliation of Author(s):西北大学电子信息学院

Teaching and Research Group:电子科学与技术系

Journal:Electronics

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

Abstract: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.

Indexed by:Journal paper

Discipline:Engineering

First-Level Discipline:Electronic Science and Techonology

Document Type:J

Volume:6

Issue:14

Page Number:1164

Translation or Not:no

Date of Publication:2025-03-16

Included Journals:SCI

First Author:Yilin Xie

Correspondence Author:Cheng Chen*

All the Authors:Jia Liu

All the Authors:Zhihao Li

All the Authors:Shilei Tian

All the Authors:Jixin Wang

All the Authors:Wu Zhao

All the Authors:Johan Stiens