Reference-less phase retrieval of multimode fibers using a deep neural network

Qian Zhang; Yuan Sui; Dennis Pohle; Nektarios Koukourakis; Jürgen Walter Czarske; Stefan Rothe

doi:10.1117/12.2674072

5 October 2023 Reference-less phase retrieval of multimode fibers using a deep neural network

Qian Zhang, Yuan Sui, Dennis Pohle, Nektarios Koukourakis, Jürgen Walter Czarske, Stefan Rothe

Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC126550Q (2023) https://doi.org/10.1117/12.2674072
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

Modal crosstalk is an issue limiting the deployment of multimode fibers (MMF) in the field of communications. Wavefront shaping techniques can compensate for the scrambling. However, the required coherent measurements usually need a complex optical system. In this paper, we introduce a deep learning-based reference-less method to undo the distortion and perform information transmission through MMF. A deep neural network trained with synthetic data is able to experimentally detect both amplitude and phase information of the light field. By using a spatial light modulator, a desired light field distribution is obtained at the output of MMF.

Conference Presentation

Citation Download Citation

Qian Zhang, Yuan Sui, Dennis Pohle, Nektarios Koukourakis, Jürgen Walter Czarske, and Stefan Rothe "Reference-less phase retrieval of multimode fibers using a deep neural network", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550Q (5 October 2023); https://doi.org/10.1117/12.2674072

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