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

Tianying Lin; Ang Liu; Xiaopei Zhang; He Li; Liping Wang; Hailong Han; Ze Chen; Xiaoping Liu; Haibin Lü

doi:10.3788/COL201917.100603

Journals >Chinese Optics Letters >Volume 17 >Issue 10 >Page 100603 > Article

Chinese Optics Letters
Vol. 17, Issue 10, 100603 (2019)

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

Tianying Lin^1,2, Ang Liu^1,2, Xiaopei Zhang^1,2, He Li^1,2..., Liping Wang^1,2, Hailong Han^1,2, Ze Chen^1,2, Xiaoping Liu^1,2,* and Haibin Lü^1,2,**|Show fewer author(s)

Author Affiliations

¹National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, China

²Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China

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DOI: 10.3788/COL201917.100603 Cite this Article Set citation alerts

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," Chin. Opt. Lett. 17, 100603 (2019) Copy Citation Text

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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.