Single trench fiber (STF) is one kind of promising novel fibers. In this paper, we design and fabricate a piece of ytterbium-doped STF. The core diameter of the homemade STF is 30 μm and the cladding diameter is 250 μm. Based on this self-developed STF, we have constructed an all-fiberized fiber amplifier that is operating under a continuous-wave regime at 1070 nm wavelength. The maximum output power of the system reaches 1.5 kW, which, to the best of our knowledge, is the highest output power of STF-based laser systems. The M2 is measured to be 1.65 at 1.36 kW and 1.92 at the highest output power respectively. The slope efficiency of amplification system is 68%. The performance of the system can be further enhanced by optimizing the fiber design and system structure.
In this report, we will introduce our recent advances in developing the deep-learning-based coherent fiber laser array systems for power scaling and spatial light structuring. Our motivation is to construct a deep-learning network for estimating the thermal and environmental induced phase errors, and further compensate the phase errors by the phase control servo with the assistance of the network outputs. Technical progresses in terms of the network optimization, two-stage control scheme, and optical field information acquisition will be covered. Moreover, the prospects and challenges towards the future implementation of intelligent control for CBC systems will be discussed.
All-solid photonic bandgap fiber (AS-PBGF) has been an unrivalled platform for the effective mode area (EMA) scaling of large-mode-area fiber and the selective spectral filtering. Super-large EMA scheme assisted by the multiple resonance mechanism is also achievable while maintaining the robust single-mode (RSM) operation. In the current work, we have proposed aother modified multi-resonant AS-PBGF with some high-index nodes are replaced by the background material. By extending the multi-resonant coupling concept, a specially designed microstructural cladding, with multiresonant cores in the inner layers and leakage channels in the outermost layer, is employed to generate broadband resonance and modal dissipation of high-order-modes (HOMs) under bent configuration. Sufficient confinements on the modal distribution of fundamental mode (FM) are retained by adjusting the arrangement of Ge-doped rods in the microstructure cladding precisely, and the rotational symmetry of the proposed AS-PBGF makes it insensitive to bending direction. The missing Ge-doped rods in each layer are properly designed to stress differential bending loss between FM and HOMs with high loss ratio. Bending loss of FM less than 0.05 dB/m and high loss ratio over 200 times are always available and independent of bending direction. An EMA greater than 900 μm2 and a loss ratio up to ~ 495 can be obtained under the bending radius of 45 cm.
In this study, we design and fabricate a novel type of active fiber——double-tapered double-clad fiber (DT-DCF). Based on this self-developed DT-DCF, we have constructed an all-fiberized fiber amplifier that is operating under a continuous-wave (CW) regime at 1080 nm wavelength. The maximum output power of the system reaches 4 kW, which, to the best of our knowledge, is the highest output power of tapered fiber-based laser systems. The amplifier exhibits near-single-mode beam quality (M2=1.33) at the highest output power with a slope efficiency of 83%. Our result successfully verifies the potential of power scalability of DT-DCF, and the performance of our system can be further enhanced by fiber design optimization.
Agile mode switching between LP01 mode and LP11 mode and power amplification in specific mode can be achieved at the same time by inserting an acoustically-induced fiber grating into a Raman fiber amplifier. The maximum signal light output power of the LP01 mode and LP11 mode is 8.51W and 7.98 W with the characteristic frequency of 769 kHz and 763 kHz, respectively. Moreover, the ratio of the two modes can be adjusted by modulating the frequency loaded on the acoustically-induced fiber grating. This work could provide an example of realizing a mode-switchable Raman fiber amplifier for practical application.
Mode decomposition (MD) is essential to reveal the intrinsic mode properties of fiber beams. However, traditional numerical MD approaches are relatively time-consuming and sensitive to the initial values. To solve these problems, deep learning technique is introduced to perform non-iterative MD. In this paper, we focus on the real-time MD ability of the pre-trained convolutional neural network. The numerical simulation indicates that the averaged correlation between the reconstructed patterns and measured patterns is 0.9987 and the decomposing rate can reach about 125 Hz. As for the experimental case, the averaged correlation is 0.9719 and the decomposing rate is 29.9 Hz, which is restricted by the maximum frame rate of the CCD camera. The results of both simulation and experiment show the superb real-time ability of the deep learning-based MD methods.
We introduce deep learning technique to perform robust mode decomposition (MD) for few-mode optical fiber. Our goal is to learn a robust, fast and accurate mapping from near-field beam profiles to the complete mode coefficients, including both of the modal amplitudes and phases. Taking a few-mode fiber which supports 3 linearly polarized modes into consideration, simulated near-field beam profiles with known mode coefficient labels are generated and fed into the convolutional neural network (CNN) to carry out the training procedure. Further, saturated patterns are added into the training samples to increase the robustness. When the network gets convergence, ordinary and saturated beam patterns are both utilized to perform MD with pre-trained CNN. The average correlation value of the input and reconstructed patterns can reach as high as 0.9994 and 0.9959 respectively for two cases. The consuming time of MD for one beam pattern is about 10ms. The results have shown that deep learning techniques highly favors the accurate, robust and fast MD for few-mode fiber.
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