Learning to decompose the modes in few-mode fibers with deep convolutional neural network

被引:135
|
作者
An, Yi [1 ]
Huang, Liangjin [1 ]
Li, Jun [1 ]
Leng, Jinyong [1 ]
Yang, Lijia [1 ]
Zhou, Pu [1 ]
机构
[1] Natl Univ Def Technol, Coll Adv Interdisciplinary Studies, Changsha 410073, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
POWER; SOLITONS; BEAM;
D O I
10.1364/OE.27.010127
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
We introduce a deep-learning technique to perform complete mode decomposition for few-mode optical fibers for the first time. Our goal is to learn a fast and accurate mapping from near-field beam patterns to the complete mode coefficients, including both modal amplitudes and phases. We train the convolutional neural network with simulated beam patterns and evaluate the network on both the simulated beam data and the real beam data. In simulated beam data testing, the correlation between the reconstructed and the ideal beam patterns can achieve 0.9993 and 0.995 for 3-mode case and 5-mode case, respectively. While in the real 3-mode beam data testing, the average correlation is 0.9912 and the mode decomposition can be potentially performed at 33 Hz frequency on a graphic processing unit, indicating real-time processing ability. The quantitative evaluations demonstrate the superiority of our deep learning-based approach. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
引用
收藏
页码:10127 / 10137
页数:11
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