Compensating for phase drifts in holographic measurements

Digital holography allows measuring the complex amplitude of a given wavefront. We presented in detail the off-axis holography approach. However, it requires a separate reference arm. Due to air flow, vibrations, or other perturbations, the optical path length difference between the two arms can fluctuate in time, even in controlled lab experiments on a good optical table. This means that the phase of the measured wavefront is estimated up to a global phase that can randomly change over time. This is very detrimental for transmission matrix measurements as the relative phase between each column has to be precisely estimated. This is particularly true when the measurement time can take few minutes or more when using a liquid crystal spatial light modulator that has a limited frame rate. In [R. Mouthaan et al., Appl. Opt. (2022)], the authors propose a simple yet robust way to compensate for phase fluctuations, even when the phase changes completely between two frames.

Spatiotemporal control of light

Joel A. Carpenter
October 2021

Gerchberg-Saxton is a phase retrieval algorithm, which attempts to retrieve the phase corresponding to two intensity images taken in the near and far-field respectively. It can also be used for calculating computer-generated holograms (phase masks) that generate a desired.

Controlling a Spatial Light Modulator remotely using a Raspberry Pi

Standard phase spatial light modulators (SLMs) have the advantage of being controlled as a secondary display. It saves money on dedicated control interfaces and simplifies the usage. However, when trying to control an experiment remotely, which is especially needed these days, It adds some complexity when using remote desktop software or ssh. I will detail here how to use a Raspberry Pi to control an SLM and send images from a computer on the same local network. The computer that controls the experiment can now easily be controlled from the comfort of your home.

Fast numerical estimations of axisymmetric multimode fibers modes

Estimating the propagation constants and the transverse mode profiles of multimode fibers is not as easy as it sounds. In our recent work we highlighted here, we needed to estimate the mode profiles for a standard graded-index fiber. It turned out that many standard approximations done in the literature to estimate the propagation constants do not give results accurate enough for the mode profile. The general approach we introduced in a previous tutorial to numerically find the fiber modes for any index profile using a 2D scalar finite differences approach is still valid. However, to provide accurate results, it needs a fine discretization of the space that leads to important memory and computational time requirements when the fiber core increases. If we consider an axisymmetric fiber, we can obtain a 1D formulation of the problem, that is unfortunately unstable under naive finite differences approaches. We detail here a stable formulation that leads to accurate and fast estimations of the mode profiles.