Experimental validation for high-order vector-eigenmode decomposition with polarization characteristics  

作　　者：Huihui Zhao Siyan Wang Yancheng Huang Wei Chen Fufei Pang Xianglong Zeng 赵慧慧;王思俨;黄彦承;陈伟;庞拂飞;曾祥龙(Key Laboratory of Specialty Fiber Optics and Optical Access Networks,Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai Institute for Advanced Communication and Data Science,Shanghai University,Shanghai 200444,China)

机构地区：[1]Key Laboratory of Specialty Fiber Optics and Optical Access Networks,Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai Institute for Advanced Communication and Data Science,Shanghai University,Shanghai 200444,China

出　　处：《Chinese Optics Letters》2024年第11期13-17,共5页中国光学快报(英文版)

基　　金：supported by the National Natural Science Foundation of China (Nos.12274281,U2241237,and 62275148);the Open Research Fund of State Key Laboratory of Advanced Optical Communication Systems and Networks,Shanghai Jiao Tong University (No.2023GZKF021);the Jiangsu Provincial Industry Outlook and Key Core Technologies Key Projects (No.BE2022055-4);111 Project (No.D20031)。

摘　　要：Vector vortex beams(VVBs) have attracted considerable attention due to their unique polarization distribution and helical phase wavefront. We first attempt to retrieve the modal coefficients of hybrid VVBs measured by their multiplex polarized intensities using the deep learning(DL)-based stochastic parallel gradient descent(SPGD) algorithm. The Xception-based DL model with multi-view images can make an accurate prediction of modal coefficients that are validated by the theoretical calculations of the waveplate angles, demonstrating a high correlation of 99.65%. The universality of the algorithm to highorder vector-eigenmodes(VMs) decomposition is proved to enable the precise reconstruction of modal patterns generated by mode-selective couplers, which promotes the accurate characteristics of VVBs in laser beam characterization and fiber mode-division multiplexing.

关 键 词：vector eigenmode polarization characteristics mode decomposition deep learning SPGD algorithm. 

分 类 号：TS1[轻工技术与工程—纺织科学与工程]