All-fiber wavefront shaping by transmission matrix engineering

[S. Resisi et al., APL Photonics, 5 (2020)]

In the past 10 years, many applications were successfully demonstrated for wavefront shaping in multimode fibers, from endoscopic to telecommunications through optical tweezers. However, these techniques require to modulate the incident field using free space modulators. In the present paper, S. Resisi and co-authors introduce a new approach that relies on modulating the transmission matrix itself by applying changes that modify its boundary conditions. Using an all-fiber apparatus, focusing light at the distal end of the fiber and conversion between fiber modes is demonstrated. Since in this approach the number of degrees of control can be larger than the number of fiber modes, it allows simultaneous control over multiple inputs and multiple wavelengths.

Compare Different Methods of Modes Estimation of Bent Multimode Fibers with pyMMF

In a previous tutorial, I explained how to calculate the modes of a bent multimode fibers. I introduced two methods, following the approach published in [M. Plöschner, T. Tyc, and T. Čižmár, Nat. Photon. (2015)]. In this short tutorial, I show how to use pyMMF to simulate bent fibers and compare the two different methods. A Jupyter notebook can be found on my Github account: compare_bending_methods.ipynb.

Wavefront shaping in complex media for analog computation

[M. W. Matthès et al., Optica, 6 (2019)]

Performing linear operations using optical devices is a crucial building block in many fields ranging from telecommunications to optical analog computation and machine learning. For many of these applications, key requirements are robustness to fabrication inaccuracies, reconfigurability, and scalability. Traditionally, the conformation or the structure of the medium is optimized in order to perform a given desired operation. Since the advent of wavefront shaping, we know that the complexity of a given operation can be shifted toward the engineering of the wavefront, allowing, for example, to use any random medium as a lens.

pyMMF:  Simulating Multimode Fibers in Python

Part 1: Step Index Benchmark

I recently published a two-part tutorial on how to find the modes of an arbitrary multimode fiber without or with bending. Based on this tutorial, I published a (still experimental) version of a Python module to find the modes of multimode fibers and calculate their transmission matrix: pyMMF. The goal of this module is not to compete with commercial solutions in terms of precision but to provide a way to easily simulate realistic fiber systems. To validate the approach, I use step-index multimode fibers as a benchmark test as the dispersion relation is analytically known (see my tutorial here) and for which the Linearly Polarized (LP) mode approximation yields good results. I focus my attention here on the precision of the numerically found propagation constants.