Dual-Output Mode Analysis of Multimode Laguerre-Gaussian Beams via Deep Learning

Optics

Yongyuan Zhu¹  Ruizhi Zhao¹  Yaguang Xu¹  Jincheng Zou¹  Chao Zhang¹  Ronger Lu¹  Xia Feng¹  Yongchuang Chen¹  Yiqiang Qin¹  Xuhao Hong¹  Xudong Yuan¹ 

[1] National Laboratory of Solid State Microstructures and Collaborative Innovation, Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China;

关键词: mode analysis;    orbital angular momentum;    Laguerre-Gaussian beam;    convolutional neural network;    deep learning;   

DOI  :  10.3390/opt2020009

来源: DOAJ

【 摘 要 】

The Laguerre-Gaussian (LG) beam demonstrates great potential for optical communication due to its orthogonality between different eigenstates, and has gained increased research interest in recent years. Here, we propose a dual-output mode analysis method based on deep learning that can accurately obtain both the mode weight and phase information of multimode LG beams. We reconstruct the LG beams based on the result predicted by the convolutional neural network. It shows that the correlation coefficient values after reconstruction are above 0.9999, and the mean absolute error (MAE) of the mode weights and phases are about 1.4 ${\times 10}^{-3}$ and 2.9 ${\times 10}^{-3}$, respectively. The model still maintains relatively accurate prediction for the associated unknown data set and the noise-disturbed samples. In addition, the computation time of the model for a single test sample takes only 0.975 ms on average. These results show that our method has good abilities of generalization and robustness and allows for nearly real-time modal analysis.

【 授权许可】

Unknown