Maximum information states for coherent scattering measurements

[D. Bouchet et al., Nat. Phys., 71 (2021)]

Coherent light is a popular tool for sensing and imaging. In simple cases, one can guess or compute a spatial and/or temporal excitation beam profile that ensures that the information about a specific target can be retrieved. However, there was no general rule to find the optimal states of light that maximize the precision of a given parameter estimation. Moreover, such states are expected to depend heavily on the parameter one tries to measure. In the present paper, the authors define a general framework to identify such optimal spatial channels, regardless of the complexity of the propagating medium, using the measurement of the transmission matrix. They demonstrate this concept using wavefront shaping to probe the phase and the position of a target hidden behind a static scattering medium.

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.

Inverse design of planar optical components using deep learning

[N.J. Dinsdale et al., arxiv, 2009.11810, (2020)]

Photonic integrable circuits are basically waveguide structures that allow performing useful operations, such as mode or wavelength multiplexing/demultiplexing in the case of telecommunication applications. For many operations, we can find quite easy solutions, where the shape of the structure imposes certain boundary conditions that force light to behave the way we want. However, for an arbitrary operation, it is not always possible to find a trivial solution. Non-trivial solutions, where the link between the geometry of the structure and its function is not direct, should then be considered. In the present paper, the authors use deep learning to find geometrical configurations for planar photonic circuits that look like disordered waveguides but actually perform a previously chosen linear operation. These configurations lead experimentally to robust, high throughput, and accurate behaviors.

Postdoctoral position: Matrix approach for resonant multiple scattering of light (theory)

Langevin Institute, Paris

We propose a 2 years postdoctoral position in the Waves Theory and Mesoscopic Physics group of the Langevin Institute under the supervision of Arthur Goetschy. The goal of the project is to provide a theory for the scattering matrix of strongly scattering media made of resonant units for wavefront shaping applications. Applicants should have a Ph.D. in wave physics with a solid background on wave propagation in complex systems.
Contact: arthur.goetschy@espci.psl.eu

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