Article Dans Une Revue Optics Express Année : 2026

On-demand orbital angular momentum modes through hollow-core multimode fiber via a neural network model

Résumé

We present a machine-learning approach to generate and dynamically reconfigure orbital angular momentum (OAM) beams in a multimode inhibited-coupling hollow-core photonic crystal fiber (IC-HCPCF). To achieve this, we trained a neural network-based digital twin for a 139 µm core IC-HCPCF guiding over 60 LP-like modes per polarization in the green spectral range. The accuracy of the neural network was confirmed by the high fidelity between experimental and predicted output intensity patterns in Fourier-conjugate planes, with median Pearson correlation coefficients exceeding 98%. These results highlight multimode IC-HCPCFs as a versatile platform for structured light transport and on-demand beam shaping.

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hal-05527179 , version 1 (25-02-2026)

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Siwar Gharbi, Jérémy Saucourt, Kostiantyn Vasko, Benoit Debord, Frédéric Gerome, et al.. On-demand orbital angular momentum modes through hollow-core multimode fiber via a neural network model. Optics Express, 2026, 34 (1), pp.1121. ⟨10.1364/oe.580998⟩. ⟨hal-05527179⟩
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