In this manuscript we demonstrate a method to reconstruct the wavefront of focused beams from a measured diffraction pattern behind a diffracting mask in real-time. The phase problem is solved by means of a neural network, which is trained with simulated data and verified with experimental data. The neural network allows live reconstructions within a few milliseconds, which previously with iterative phase retrieval took several seconds, thus allowing the adjustment of complex systems and correction by adaptive optics in real time. The neural network additionally outperforms iterative phase retrieval with high noise diffraction patterns.
@article{arxiv.2101.05082,
title = {Real-time phase-retrieval and wavefront sensing enabled by an artificial neural network},
author = {Jonathon White and Sici Wang and Wilhelm Eschen and Jan Rothhardt},
journal= {arXiv preprint arXiv:2101.05082},
year = {2021}
}