# Wavefront shaping techniques in complex media

- Details
- Category: Highlights
- Published on Monday, 15 April 2019 13:50

{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}}

\)

## Wavefront shaping in complex media for analog computation [highlight]

[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 analogue 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.

In [M. W. Matthès et al., Optica, 6 (2019)], we propose to use this approach to use complex optical media such as multimode fibers or scattering media as a computational platform driven by wavefront shaping to perform analogue linear operations. Given a large random transmission matrix representing the light propagation in such a medium, we can extract any desired smaller linear operator by finding suitable input and output projectors. We demonstrate this concept by finding input wavefronts using a Spatial Light Modulator that cause the complex medium to act as a desired complex-valued linear operator on the optical field.

- Details
- Category: Multimode fibers
- Published on Monday, 25 February 2019 16:20

{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.

- Details
- Category: Events
- Published on Monday, 18 February 2019 16:47

## Complex Nanophotonics Science Camp 2019

### Aug 11th - Aug 14th 2019

### Cumberland Lodge

### Windsor, United Kingdom

**Organizers: **Jacopo Bertolotti (University of Exeter, UK), Paloma Arroyo Huidobro (Imperial College London, UK), Giorgio Volpe (University College London, UK), Kevin Vynck (LP2N , Bordeaux, France)

## Link: here

- Details
- Category: Events
- Published on Thursday, 14 February 2019 11:22

## PIERS2019 Rome Focus/Special Sessions

## Disordered Photonics

### June 17th - June 20th 2019

### Faculty of Engineering - University of Rome "La Sapienza"

### Rome, Italy

**Organizers: **Dr. Pedro David Garcia Fernandez (Catalan Institute of Nanoscience and Nanotechnology - ICN2) and Dr. Jacopo Bertolotti (University of Exeter)

## Link: here

- Details
- Category: Events
- Published on Friday, 08 February 2019 14:17

## Summer School

## Imaging in Wave Physics: Multi-Wave and Large Sensor Networks

### Sep 23rd - Sep 27th 2019

### Institut d’Études Scientifiques de Cargèse

### Corsica, France

**Director: **Mathias Fink (Institut Langevin, ESPCI, France)

**Organizers: **Alexandre Aubry, Romain Pierrat, Sébastien M. Popoff (Institut Langevin, CNRS - ESPCI, France)

## Link: here

- Details
- Category: Multimode fibers
- Published on Saturday, 29 December 2018 16:35

{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] Numerical Estimation of Multimode Fiber Modes and Propagation Constants:

## Part 2: Bent Fibers

We saw in the first part of the tutorial that the profiles and the propagation constants of the propagation modes of a straight multimode fiber can easily be avulated for an arbitrary index profile by inverting a large but sparse matrix. Under some approximations [1], a portion of fiber with a fixed radius of curvature satisfies a similar problem that can be solved with the same numerical tools, as we illustrate with the PyMMF Python module [2]. Moreover, when the modes are known for the straight fiber, the modes for a fixed radius can be approximate by inverting a square matrix of size the number of propagating modes [1]. It allows fast computation of the modes for different radii of curvature.