Publication
Transmission in Multimode fiber with deep learning
Demetri Psaltis, Christophe Moser, Damien Claude-Marie Loterie, Babak Rahmani, Georgia Konstantinou
2018
Conference paper
2018
Conference paper
Abstract
Spatio-temporal control of femtosecond pulse-delivery through multimode fibers (MMF) can be used to achieve two photon photo-polymerization. Transmission-matrix method is used in linear domain to solve the scrambling effect at the fiber output and generate focused spots. However, multimode fibers suffer from non-linear effects at high peak intensities. The method is not effective to describe light propagation in the nonlinear regime. Here we propose a deep learning network which can learn the relationship between inputs and outputs of the MMF. We show that once the network is properly trained, it can directly calculate the inverse of the transmission matrix.
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Related concepts (33)
Fiber laser
A fiber laser (or fibre laser in Commonwealth English) is a laser in which the active gain medium is an optical fiber doped with rare-earth elements such as erbium, ytterbium, neodymium, dysprosium, praseodymium, thulium and holmium. They are related to doped fiber amplifiers, which provide light amplification without lasing. Fiber nonlinearities, such as stimulated Raman scattering or four-wave mixing can also provide gain and thus serve as gain media for a fiber laser.
Transmission line
In electrical engineering, a transmission line is a specialized cable or other structure designed to conduct electromagnetic waves in a contained manner. The term applies when the conductors are long enough that the wave nature of the transmission must be taken into account. This applies especially to radio-frequency engineering because the short wavelengths mean that wave phenomena arise over very short distances (this can be as short as millimetres depending on frequency).
Deep learning
Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
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