Deep learning enabled real-time modal analysis for fiber beams

Yi An; Liangjin Huang; Jun Li; Jinyong Leng; Lijia Yang; Pu Zhou

doi:10.1117/12.2541268

18 December 2019 Deep learning enabled real-time modal analysis for fiber beams

Yi An, Liangjin Huang, Jun Li, Jinyong Leng, Lijia Yang, Pu Zhou

Proceedings Volume 11342, AOPC 2019: AI in Optics and Photonics; 1134203 (2019) https://doi.org/10.1117/12.2541268
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

Abstract

Mode decomposition (MD) is essential to reveal the intrinsic mode properties of fiber beams. However, traditional numerical MD approaches are relatively time-consuming and sensitive to the initial values. To solve these problems, deep learning technique is introduced to perform non-iterative MD. In this paper, we focus on the real-time MD ability of the pre-trained convolutional neural network. The numerical simulation indicates that the averaged correlation between the reconstructed patterns and measured patterns is 0.9987 and the decomposing rate can reach about 125 Hz. As for the experimental case, the averaged correlation is 0.9719 and the decomposing rate is 29.9 Hz, which is restricted by the maximum frame rate of the CCD camera. The results of both simulation and experiment show the superb real-time ability of the deep learning-based MD methods.

Citation Download Citation

Yi An, Liangjin Huang, Jun Li, Jinyong Leng, Lijia Yang, and Pu Zhou "Deep learning enabled real-time modal analysis for fiber beams", Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 1134203 (18 December 2019); https://doi.org/10.1117/12.2541268

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