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.

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In this manuscript, we demonstrate high power, all-fiberized and polarization-maintained amplifiers with narrow linewidth and near-diffraction-limited beam quality by simultaneously suppressing detrimental stimulated Brillouin scattering (SBS) and mode instability (MI) effects. Compared with strictly single frequency amplification, the SBS threshold is scaled up to 12 dB, 15.4 dB, and higher than 18 dB by subsequently using three-stage cascaded phase modulation systems. Output powers of 477 W, 1040 W, and 1890 W are achieved with full widths at half maximums (FWHMs) of within 6 GHz, ~18.5 GHz, and ~45 GHz, respectively. The MI threshold is increased from ~738 W to 1890 W by coiling the active fiber in the main amplifier. Both the polarization extinction ratio (PER) and beam quality (M² factor) are maintained well during the power scaling process. To the best of our knowledge, this is the first demonstration of all-fiberized amplifiers with narrow linewidth, near linear polarization, and near-diffraction-limited beam quality at 2 kW power-level.

Coherent polarization beam combining (CPBC) of fiber lasers has the potential to scale the output total power while simultaneously maintaining good beam quality. In this paper, we will present the very recent technical advance in a single-channel coherently combinable linearly polarized narrow-linewidth fiber amplifier and high-power CPBC system. The noise property of a recently developed near 2 kW fiber amplifier and its feasibility in a CPBC system, CPBC of four 500-W-level fiber amplifiers and two kilowatt fiber amplifiers are demonstrated for the first time, which is also the first result for a 2 kW CPBC system. We have also performed numerical analysis on the performance scaling of CPBC, and deduced handy design guidelines for CPBC of a 64-element high-power system.

In recent years, machine learning, especially various deep neural networks, as an emerging technique for data analysis and processing, has brought novel insights into the development of fiber lasers, in particular complex, dynamical, or disturbance-sensitive fiber laser systems. This paper highlights recent attractive research that adopted machine learning in the fiber laser field, including design and manipulation for on-demand laser output, prediction and control of nonlinear effects, reconstruction and evaluation of laser properties, as well as robust control for lasers and laser systems. We also comment on the challenges and potential future development.

In this paper, a spectral model by incorporating SRS effect is proposed and established, which is feasible for analyzing the SRS effect both in high-power fiber oscillator and master oscillator power amplifier (MOPA) system. The theoretical results show that the SRS effect is tightly related to the bandwidths of the fiber Bragg gratings (FBGs) and it can be efficiently suppressed by optimizing the bandwidth of the FBGs. Besides, the established theoretical model is also feasible for analyzing the influence of seed power on the SRS effect. The theoretical predictions agree well with the previous experimental results.