Call for papers on Wavefront Shaping Tutorials:
JPhys Photonics Special Issue

Guest Editors

• Ivo Vellekoop - University of Twente, Netherlands
• Joshua Brake - Harvey Mudd College, United States
• Sébastien Popoff - CNRS - Institut Langevin - ESPCI, France

In the past 15 years, wavefront shaping has emerged as a preferred tool for controlling and studying light propagation in complex media. Thousands of papers have been published, many of which present new and potentially exciting applications. However, wavefront shaping is a tool that requires experience, custom codes, and most importantly, specific tricks, which are often not published or shared. This special issue provides an opportunity to disseminate this information, thereby ensuring the reproducibility of the results and promoting the spread of techniques in this field.

More information here

Learning and Avoiding Disorder in Multimode Fibers

Sébastien M. Popoff
July 2021

In this work, we demonstrate the existence of a set of spatial channels in multimode fibers that are robust to strong local perturbations. We show that, even for a high level of disorder, light propagation can be characterized by just a few key properties.

Related article: doi.org/10.1103/PhysRevX.11.021060

Controlling the propagation of light in Multimode Fibers

Maxime Matthès, Ph.D. defense
February 12th 2021

Learning and avoiding disorder in multimode fibers

[M.W. Matthès, arxiv, 2010.14813 (2020)]

In the past 10+ years, numerous advances were made for endoscopic imaging, micromanipulation, or telecommunication applications with multimode fiber. The main limitation to real-life applications is the sensitivity to perturbations that sometimes causes the transmission property of the fiber to change in real-time. To address this issue, the authors (we) show that, even in the presence of strong perturbations, there exists a set of channels that are almost insensitive to perturbations. Interestingly, these channels can be found using only measurements from small perturbation leveraging the so-called generalized Wigner-Smith operator. This requires the measurement of the transmission matrix, which is done thanks to a new technique based on deep learning frameworks that compensate automatically for misalignments and aberrations, allowing fast and easy acquisitions.