\(
\def\ket#1{{\left|{#1}\right\rangle}}
\def\bra#1{{\left\langle{#1}\right|}}
\def\braket#1#2{{\left\langle{#1}|{#2}\right\rangle}}
\)

[tutorial] Compare Different Methods of Modes Estimation of Bent Multimode Fibers with pyMMF

bent small
 

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

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{jcomments on}

\(
\def\ket#1{{\left|{#1}\right\rangle}}
\def\bra#1{{\left\langle{#1}\right|}}
\def\braket#1#2{{\left\langle{#1}|{#2}\right\rangle}}
\)

[tutorial] 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 term 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.

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