Machine learning-driven complex models for wavefront shaping through multimode fibers
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913573558550528 |
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| author | Saucourt, Jérémy Gobé, Benjamin Helbert, David Desfarges-Berthelemot, Agnès Kermène, Vincent |
| author_facet | Saucourt, Jérémy Gobé, Benjamin Helbert, David Desfarges-Berthelemot, Agnès Kermène, Vincent |
| contents | We investigate a method to retrieve full-complex models (Transmission Matrix and Neural Network) of a highly multimode fiber (140 LP modes/polarization) using a straightforward machine learning approach, without the need of a reference beam. The models are first validated by the high fidelity between the predicted and the experimental images in the near field and far field output planes (Pearson correlation coefficient between 97.5% and 99.1% with our trained Transmission Matrix or Neural Network). Their accuracy was further confirmed by successful 3D beam shaping, a task achievable only with a true full complex model. As a prospect, we also demonstrate the ability of our neural network architecture to model nonlinear Kerr propagation in gradient index multimode fiber and predict the output beam shape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04531 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning-driven complex models for wavefront shaping through multimode fibers Saucourt, Jérémy Gobé, Benjamin Helbert, David Desfarges-Berthelemot, Agnès Kermène, Vincent Optics We investigate a method to retrieve full-complex models (Transmission Matrix and Neural Network) of a highly multimode fiber (140 LP modes/polarization) using a straightforward machine learning approach, without the need of a reference beam. The models are first validated by the high fidelity between the predicted and the experimental images in the near field and far field output planes (Pearson correlation coefficient between 97.5% and 99.1% with our trained Transmission Matrix or Neural Network). Their accuracy was further confirmed by successful 3D beam shaping, a task achievable only with a true full complex model. As a prospect, we also demonstrate the ability of our neural network architecture to model nonlinear Kerr propagation in gradient index multimode fiber and predict the output beam shape. |
| title | Machine learning-driven complex models for wavefront shaping through multimode fibers |
| topic | Optics |
| url | https://arxiv.org/abs/2411.04531 |