We introduce a deep-learning technique to perform complete mode decomposition for few-mode optical fibers for the first time. Our goal is to learn a fast and accurate mapping from near-field beam patterns to the complete mode coefficients, including both modal amplitudes and phases. We train the convolutional neural network with simulated beam patterns and evaluate the network on both the simulated beam data and the real beam data. In simulated beam data testing, the correlation between the reconstructed and the ideal beam patterns can achieve 0.9993 and 0.995 for 3-mode case and 5-mode case, respectively. While in the real 3-mode beam data testing, the average correlation is 0.9912 and the mode decomposition can be potentially performed at 33 Hz frequency on a graphic processing unit, indicating real-time processing ability. The quantitative evaluations demonstrate the superiority of our deep learning-based approach. IntroductionRecently, few-mode fibers (FMFs) have attracted much attention for both fundamental and applied research. Space division multiplexing based on FMFs is a promising way to overcome the anticipated capacity crunch of the single-mode fibers [1]. Larger mode area provided by FMFs helps to suppress the detrimental nonlinear effects and improve the damage threshold, which paves the way to higher power fiber lasers [2]. Furthermore, FMF is a perfect platform for experimental exploration on the complicated spatiotemporal soliton dynamics [3,4] and new nonlinear phenomena [5,6] in multi-mode fibers. With the rapid research progress of FMF, it is highly demanded to characterize the properties of the spatial modes emitting from the FMF, which is named as mode decomposition (MD) technique. With MD techniques, the amplitude and phase information of each eigenmode in the optical fiber can be estimated, providing the complete optical field and the beam properties associated with the field, e.g. wave front [7] and beam propagation factor [8]. Recent years, MD techniques have been widely used in many applications, such as optimizing fiber-to-fiber coupling [9], analyzing mode-resolved gain [10,11] or bend loss [12], diagnosing temporal mode instabilities [13,14], measuring mode transfer matrix [15,16] and realizing adaptive mode control [17,18].In the past few years, various MD methods have been proposed with different techniques, such as spatially and spectrally resolved imaging [19], frequency domain cross-correlated imaging [20], ring-resonators [21], low coherence interferometry [22], correlation filter [23] and digital holography [24]. Although these methods can achieve accurate results, they require consuming post-data processing or intense experimental measurements. Besides these approaches, numerical computing-based MD methods have shown their equal accuracy without complex experimental operations [25][26][27][28]. modes based on weak-guidance approximation [34] and the number of them supported within the fiber depends on the fiber parameters. The purpose of the MD is to predict 2 n ρ and n θ from...

Coherent beam combination of a 1.08 kW fiber amplifier array has been demonstrated for the first time, to our knowledge. In the experiment, nine fiber amplifiers are tiled into a 3×3 array, and the output power of each amplifier is approximately 120 W. A single frequency dithering algorithm is used to compensate the phase noises between the elements, which runs on a signal processor based on field programmable gate array for phase control on the fiber amplifiers. When the phase control system goes into closed loop, the fringe contrast of the far-field intensity pattern is improved to more than 85%, and the residual phase error is less than λ/15.

Today a specific attention has been paid to look into the modal characteristics of the high-power laser beam. And the instantaneous monitoring and analyzing on modal content via the mode decomposition technique will provide a novel route. We implement the first-ever experimental investigation on the real-time mode decomposition technique for few-mode laser beam based on stochastic parallel gradient descent algorithm. It will reduce the cost and the complexity of the mode decomposition system. We have succeeded to decompose the mode spectra as well as calculating the beam quality factor at about 9 Hz monitoring rate, while the high agreement between the measured and reconstructed intensity profiles in each frame indicating the high accuracy and stability during the process. By employing a fiber-squeezing-based polarization controller, the modal content under test can be time-varying automatically.

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With an increasing output power, the power scaling of monolithic fiber laser oscillators faces the severe limitations of stimulated Raman scattering (SRS) and the transverse mode instability (TMI) effect. In this work, we report a high power monolithic fiber laser oscillator with a maximum output power of 5.2 kW, which is realized with a trade-off design between the SRS and TMI. The monolithic fiber laser oscillator is constructed with ytterbium-doped fiber with a core/inner cladding diameter of 25/400 µm and corresponding home-made FBG. High-power 915 nm laser diodes are employed as a pump source and are distributed in a bidirectional-pump configuration. By optimizing the bidirectional pump proportion, the monolithic fiber laser oscillator is scaled up to 5.2 kW with a slope efficiency of ~63%. Operating at 5.2 kW, the intensity of the Raman stokes light is ~22 dB below the signal laser and the beam quality (M2-factor) is ~2.2. To the best of our knowledge, this is a record high power for monolithic fiber laser oscillators.