Harnessing the power of complex light propagation in multimode fibers for spatially resolved sensing

TL;DR

This paper explores how complex light propagation in multimode fibers can be used for spatially resolved sensing by leveraging mode coupling and machine learning to interpret speckle patterns.

Contribution

It demonstrates that spatially dependent mode coupling is essential for spatially resolved sensing and uses machine learning to extract this information from different fiber types.

Findings

Higher spatially dependent mode coupling improves sensing accuracy

Machine learning effectively extracts spatial information from speckle patterns

Different fiber types show varying degrees of sensing performance

Abstract

The propagation of coherent light in multimode optical fibers results in a speckled output that is both complex and sensitive to environmental effects. These properties can be a powerful tool for sensing, as small perturbations lead to significant changes in the output of the fiber. However, the mechanism to encode spatially resolved sensing information into the speckle pattern and the ability to extract this information is thus far unclear. In this paper, we demonstrate that spatially dependent mode coupling is crucial to achieving spatially resolved measurements. We leverage machine learning to quantitatively extract this spatially resolved sensing information from three fiber types with dramatically different characteristics and demonstrate that the fiber with the highest degree of spatially dependent mode coupling provides the greatest accuracy.