Machine learning-driven complex models for wavefront shaping through multimode fibers

TL;DR

This paper presents a machine learning approach to accurately model wavefront propagation through multimode fibers, enabling high-fidelity image prediction and 3D beam shaping without reference beams, and extends to nonlinear Kerr effects.

Contribution

It introduces a novel machine learning method to retrieve full complex models of multimode fibers without reference beams, validated by high-fidelity predictions and successful 3D beam shaping.

Findings

High correlation between predicted and experimental images (97.5%-99.1%)

Successful 3D beam shaping using the models

Modeling of nonlinear Kerr propagation in multimode fibers

Abstract

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.