Mode instability dynamics in high-power low-numerical-aperture step-index fiber amplifier

High-performance mode decomposition using physics- and data-driven deep learning

A novel physics- and data-driven deep-learning (PDDL) method is proposed to execute complete mode decomposition (MD) for few-mode fibers (FMFs). The PDDL scheme underlies using the embedded beam propagation model of FMF to guide the neural network (NN) to learn the essential physical features and eliminate unexpected features that conflict with the physical laws. It can greatly enhance the NN's robustness, adaptability, and generalization ability in MD. In the case of obtaining the real modal weights (ρ2) and relative phases (θ), the PDDL method is investigated both in theory and experiment. Numerical results show that the PDDL scheme eliminates the generalization defect of traditional DL-based MD and the error fluctuation is alleviated. Compared with the DL-based MD, in the 8-mode case, the errors of ρ2 and θ can be reduced by 12 times and 100 times for beam patterns that differ greatly from the training dataset. Moreover, the PDDL maintains high accuracy even in the 8-mode MD case with a practical maximum noise factor of 0.12. In terms of adaptation, with a large variation of the core radius and NA of the FMF, the error keeps lower than 0.43% and 2.08% for ρ2 and θ, respectively without regenerating new dataset and retraining NN. The experimental configuration is set up and verifies the accuracy of the PDDL-based MD. Results show that the correlation factor of the real and reconstructed beam patterns is higher than 98%. The proposed MD-scheme shows much potential in the application of practical modal coupling characterization and laser beam quality analysis.

Towards Ultimate High-Power Scaling: Coherent Beam Combining of Fiber Lasers

Fiber laser technology has been demonstrated as a versatile and reliable approach to laser source manufacturing with a wide range of applicability in various fields ranging from science to industry. The power/energy scaling of single-fiber laser systems has faced several fundamental limitations. To overcome them and to boost the power/energy level even further, combining the output powers of multiple lasers has become the primary approach. Among various combining techniques, the coherent beam combining of fiber amplification channels is the most promising approach, instrumenting ultra-high-power/energy lasers with near-diffraction-limited beam quality. This paper provides a comprehensive review of the progress of coherent beam combining for both continuous-wave and ultrafast fiber lasers. The concept of coherent beam combining from basic notions to specific details of methods, requirements, and challenges is discussed, along with reporting some practical architectures for both continuous and ultrafast fiber lasers.

Sub-sampled modal decomposition in few-mode fibers

Retrieving modal contents from a multimode beam profile can provide the most detailed information of a beam. Numerical modal decomposition is a method of retrieving modal contents, and it has gained significant attention owing to its simplicity. It only requires a measured beam profile and an algorithm. Therefore, a complicated setup is not necessary. In this study, we conceived that the modal decomposition can be notably improved by data-efficiently sub-sampling the beam image instead of using full pixels of a beam profiler. By investigating the window size, the number of pixels, and algorithm for sub-sampling, the calculation time for the algorithm was faster by approximately 100 times than the case of full pixel modal decomposition. Experiments with 3-mode and 6-mode beams, which originally span 201×201 and 251×251 pixels, respectively, confirmed the remarkable improvement of calculation speed while maintaining the error function at a level of ∼10^-3. This first demonstration of sub-sampling for modal decomposition is based on the modified stochastic parallel gradient descent algorithm. However, it can be applied to other numerical or artificial intelligence algorithms and can enhance real-time analysis or active control of beam characteristics.

2 kW high-efficiency Raman fiber amplifier based on passive fiber with dynamic analysis on beam cleanup and fluctuation

In this paper, we study the power scaling in high power continuous-wave Raman fiber amplifier employing graded-index passive fiber. The maximum output power reaches 2.087 kW at 1130 nm with an optical conversion efficiency of 90.1% (the output signal power versus the depleted pump power). To the best of our knowledge, this is the highest power in the fields of Raman fiber lasers based merely on Stokes radiation. The beam quality parameter M² improves from 15 to 8.9 during the power boosting process, then beam spot distortion appears at high power level. This is the first observation and analysis on erratic dynamic properties of the transverse modes in high power Raman fiber amplifier.

Adaptive phase correction of dynamic multimode beam based on modal decomposition

We propose and demonstrate a method for the adaptive phase correction of dynamic multimode fiber beams. The phase of incident beam is reconstructed in real-time based on the complete modal information, which obtained by using the modal decomposition of correlation filter method. For the proof of principle, both of the modal decomposition and the phase correction are implemented using the same computer-generated hologram, which was encoded into a phase-only spatial light modulator. We demonstrate the phase correction of dynamic multimode beam at a rate of 5 Hz and achieve a 1.73-fold improvement on the average power-in-the-bucket. The experimental results indicate the feasibility of the real-time phase correction for the large mode area fiber laser by adaptive optics.

Learning to decompose the modes in few-mode fibers with deep convolutional neural network

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.

Transverse mode instability threshold enhancement in Yb-doped fiber lasers by cavity modification

To enhance the transverse mode instability (TMI) threshold of a fiber oscillator, a novel configuration is presented. In this configuration the oscillator cavity length is considerably reduced and the remaining active fiber is released out of the cavity to absorb the rest of the pump power and amplify the output signal of the cavity. In fiber oscillators, the index gratings generated by both forward and backward propagating signals can interact with light propagating in the backward and forward directions and degrade the output beam quality. In the proposed modified configuration, due to lower intra-cavity forward and backward signal power the index grating is smoothed and the TMI threshold is increased. Experimental results indicate that this modified configuration has a higher TMI threshold than a conventional fiber oscillator. Finally a higher TMI threshold is achieved in the bidirectional pumping scheme of the modified configuration.