Modal decomposition of complex optical fields using convolutional neural networks

TL;DR

This paper introduces a CNN-based method for modal decomposition of complex optical fields using heterodyne images, enabling accurate phase retrieval and improved robustness over traditional algorithms.

Contribution

It is the first to use complex phase information from heterodyne images with CNNs for modal decomposition, enhancing accuracy and robustness.

Findings

CNN outperforms traditional algorithms in accuracy

Method is less sensitive to beam centering

Achieves complete modal phase prediction

Abstract

Recent studies have shown convolutional neural networks (CNNs) can be trained to perform modal decomposition using intensity images of optical fields. A fundamental limitation of these techniques is that the modal phases can not be uniquely calculated using a single intensity image. The knowledge of modal phases is crucial for wavefront sensing, alignment and mode matching applications. Heterodyne imaging techniques can provide images of the transverse complex amplitude & phase profile of laser beams at high resolutions and frame rates. In this work we train a CNN to perform modal decomposition using simulated heterodyne images, allowing the complete modal phases to be predicted. This is to our knowledge the first machine learning decomposition scheme to utilize complex phase information to perform modal decomposition. We compare our network with a traditional overlap integral & center-of-mass centering algorithm and show that it is both less sensitive to beam centering and on average more accurate.