Phd Student : Dynamic shaping of laser radiation through multimode fibers for laser-matter interaction applications

Created in 2007, ALPhANOV is a technological centre member of the excellence cluster ‘Route des lasers et des hyper frequences’ and has today more than 95 employees. ALPhANOV targets the development of cutting-edge and innovative solutions, products, and techniques in the field of optics and laser technology for a variety of different markets, such as aeronautics, aerospace, medicine, luxury and military. Located in the Institute of Optics of Aquitaine, ALPhANOV is a key player in the field of optics and lasers in the Nouvelle Aquitaine region.
ALPhANOV is well reputed for its capacity to identify key-innovation technologies promoting, all along his history, the creation of new companies and spin-off (20 since the creation).

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Context of the research

This thesis subject is part of a collaboration between the Xlim laboratory and ALPhANOV, a Research and Technology Organization (RTO) based in Talence and Limoges. This thesis will be mainly carried out in the premises of the Xlim laboratory, UMR 7252 of Limoges, 123 Av. Albert Thomas, under the responsibility of Georges Humbert (Xlim), Shuwen Zeng (CNRS) and Sébastien Vergnole (ALPhANOV). Some work sessions at ALPhANOV in Talence and Limoges are also expected.

About Xlim:

Xlim research institute (www.xlim.fr) is a joint research unit held by the University of Limoges and the French CNRS. Xlim is a multidisciplinary research institute with main expertise in the domain of electronics and microwaves, photonics, mathematics, materials and computer sciences.

This Ph. D. project will be carried out within the "PHOCAL" team, which has extensive experience in controlling the propagation of coherent fields in complex systems and environments. This includes networks of lasers, multimode or multicore optical fibers, with collaborative work on the analysis of optical signals involving researchers from the laboratory specialized in numerical optimization and artificial intelligence.

State of the art: In recent years, there has been a renewed interest in multimode fibers and their applications across various fields including telecommunications (spatial multiplexing), sensors, high-power fiber laser sources, endoscopy, quantum information transmission channels, and dynamic laser beam shaping (surface treatment, machining, etc.). This interest is related to the multiple "degrees of freedom" offered by the numerous guided modes of the fiber. Manipulating these modes requires an understanding of the transformations occurring within the multimode fiber. Typically, its transmission matrix is measured interferometrically using a reference beam [1], but this process remains complex to implement. Recently, numerous studies have proposed methods to retrieve this transmission matrix or learn a neural network model, without a reference beam, solely from intensimetric speckle measurements [2]. However, these methods can only predict the intensity profile of the output beam. We are currently working on novel approaches without a reference beam, using machine learning tools capable of predicting both the amplitude and phase of the output field [3]. This research contributes to multidimensional coupling studies in linear and nonlinear regimes controlled by machine learning models, particularly paving the way for dynamic 2D or 3D profiling of beams at the end of optical fibers, in both continuous and pulsed regimes, which is pertinent for laser-material interaction applications.

Objectives: The objective of this Ph. D. thesis project is to develop processes for controlling fields transmitted through multimode fibers. Several types of fibers will be studied, targeting different applications. In the first phase, the focus will be on exploring 2D and 3D profiling to functionalize an optical fiber end by photo-polymerization. For this purpose, a double-clad optical fiber will be designed to be functional at both photo-polymerization and operation wavelength with various modal contents. In the second phase, the experimental setup (built around a silica-core multimode fiber) and the neural network structure will be evolved to design an agile intensimetric femtosecond laser source dedicated to surface texturing. In the final phase, power coupling in hollow-core multimode fibers will be studied. Analysis of the propagated field will be performed using a multicore component coupled to the fiber, which will uniquely extract the data feeding the neural network.

References

1. J. Carpenter, et al., "110x110 optical mode transfer matrix inversion," Opt. Express 22, 96-101, 2014.

2. N. Borhani, et al., "Learning to see through multimode fibers," Optica 5, pp. 960-966, 2018

3. B. Gobé et al., ‘Retrieving the complex transmission matrix of a multimode fiber by machine learning for 3D beam shaping’, Journal of Light.Tech. DOI: 10.1109/JLT.2024.3373689 (2024)

Skills and knowledge required:

• Master's degree in photonics with a strong background in optical fibers and lasers.

• Motivation to implement and develop experiments, with strong experimental skills expected.

• Good knowledge of scientific programming languages (Matlab, Python) and an interest in artificial intelligence.

• High level of autonomy and ability to organize and plan work effectively.

• Strong interpersonal skills and dynamism.

• Proficient in both written and spoken English.

• Strong motivation and curiosity.

• Ability to work in a multidisciplinary context and engage with various stakeholders