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Analyzing OAM mode purity in optical fibers with CNN-based deep learning

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Abstract

Inspired by recent rapid deep learning development, we present a convolutional-neural-network (CNN)-based algorithm to predict orbital angular momentum (OAM) mode purity in optical fibers using far-field patterns. It is found that this image-processing-based technique has an excellent ability in predicting the OAM mode purity, potentially eliminating the need of using bulk optic devices to project light into different polarization states in traditional methods. The excellent performance of our algorithm can be characterized by a prediction accuracy of 99.8% and correlation coefficient of 0.99994. Furthermore, the robustness of this technique against different sizes of testing sets and different phases between different fiber modes is also verified. Hence, such a technique has a great potential in simplifying the measuring process of OAM purity.

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DOI:10.3788/COL201917.100603

所属栏目:Fiber optics and optical communications

基金项目:This work was supported by the National Basic Research Program of China (No. 2015CB659400), the Natural Science Foundation of Jiangsu Province (No. BK20150057), and the Fundamental Research Funds for the Central Universities (No. 021314380100).

收稿日期:2019-03-22

录用日期:2019-08-22

网络出版日期:2019-09-25

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Tianying Lin:National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, ChinaCollaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
Ang Liu:National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, ChinaCollaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
Xiaopei Zhang:National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, ChinaCollaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
He Li:National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, ChinaCollaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
Liping Wang:National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, ChinaCollaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
Hailong Han:National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, ChinaCollaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
Ze Chen:National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, ChinaCollaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
Xiaoping Liu:National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, ChinaCollaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
Haibin Lü:National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, ChinaCollaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China

联系人作者:Xiaoping Liu(xpliu@nju.edu.cn); Haibin Lü( lvhaibin203@163.com);

备注:This work was supported by the National Basic Research Program of China (No. 2015CB659400), the Natural Science Foundation of Jiangsu Province (No. BK20150057), and the Fundamental Research Funds for the Central Universities (No. 021314380100).

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引用该论文

Tianying Lin, Ang Liu, Xiaopei Zhang, He Li, Liping Wang, Hailong Han, Ze Chen, Xiaoping Liu, Haibin Lü, "Analyzing OAM mode purity in optical fibers with CNN-based deep learning," Chinese Optics Letters 17(10), 100603 (2019)

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