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18 June 2024 Reference-free phase retrieval of multimode fibers enhanced by physics-driven neural network
Yuan Sui, Qian Zhang, Stefan Rothe, Jürgen Czarske
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Proceedings Volume PC13017, Machine Learning in Photonics; PC1301707 (2024) https://doi.org/10.1117/12.3017502
Event: SPIE Photonics Europe, 2024, Strasbourg, France
Abstract
Analysing the optical field of multimode fibers by intensity images machine learning for design in photonics-based sensing and imaging applications. However, existing mathematical algorithms, iterative methods, or AI algorithms encounter scalability issues. In this study, we incorporate the physics principle of mode superposition into neural networks for mode decomposition. This integration eliminates the need for an extensive amount of training data and the time-consuming training process. The proposed method, without pre-training, can effectively perform mode decomposition for up to 220 modes. With the extracted amplitude and phase information, the correlation coefficient between the reconstructed optical field and the original image surpasses 98%. Investigations with noisy data demonstrate the network's efficiency in extracting both phase and magnitude information, even when the signal-to-noise ratio of the image is as low as 1dB which is crucial for secure data communication with multimode fibers.
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Yuan Sui, Qian Zhang, Stefan Rothe, and Jürgen Czarske "Reference-free phase retrieval of multimode fibers enhanced by physics-driven neural network", Proc. SPIE PC13017, Machine Learning in Photonics, PC1301707 (18 June 2024); https://doi.org/10.1117/12.3017502
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KEYWORDS
Multimode fibers
Data modeling
Neural networks
Phase retrieval
Modal decomposition
Education and training
Evolutionary algorithms
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