Work

Real-time phase-retrieval and wavefront sensing enabled by an artificial neural network

Year: 2021

Type: article

Abstract: 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.

Source: Optics Express

DOI: 10.1364/oe.419105

Authors: Jonathon White, Sici Wang, Wilhelm Eschen, Jan Rothhardt

Institutions: Friedrich Schiller University Jena, Helmholtz Institute Jena, Fraunhofer Institute for Applied Optics and Precision Engineering

Language: English

Topic: Adaptive optics and wavefront sensing

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funder: European Social Fund