Non-mechanical axial focus tuning by coherent beam combining technique

Chang, Hongxiang; Su, Rongtao; Zhang, Yuqiu; Shu, Bowang; Long, Jinhu; Leng, Jinyong; Zhou, Pu

doi:10.1016/j.optlaseng.2023.107941

Optics and Lasers in Engineering

2024

DOI: 10.1016/j.optlaseng.2023.107941

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Non-mechanical axial focus tuning by coherent beam combining technique

Hongxiang Chang,

Rongtao Su,

Yuqiu Zhang

et al.

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Cited by 3 publications

References 34 publications

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Single-step phase identification and phase locking for coherent beam combination using deep learning

Xie,

Chernikov,

Mills

et al. 2024

Sci Rep

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Coherent beam combination offers a solution to the challenges associated with the power handling capacity of individual fibres, however, the combined intensity profile strongly depends on the relative phase of each fibre. Optimal combination necessitates precise control over the phase of each fibre channel, however, determining the required phase compensations is challenging because phase information is typically not available. Additionally, the presence of continuously varying phase noise in fibre laser systems means that a single-step and high-speed correction process is required. In this work, we use a spatial light modulator to demonstrate coherent combination in a seven-beam system. Deep learning is used to identify the relative phase offsets for each beam directly from the combined intensity pattern, allowing real-time correction. Furthermore, we demonstrate that the deep learning agent can calculate the phase corrections needed to achieve user-specified target intensity profiles thus simultaneously achieving both beam combination and beam shaping.

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Single-step phase identification and phase locking for coherent beam combination using deep learning

Xie,

Chernikov,

Mills

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

Efficient distributed architecture and optimized subarray control strategy to facilitate large-scale coherent beam combination

Qi,

Zheng,

Jiang

et al. 2024

Opt. Express

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Traditional coherent beam combination (CBC) system architecture has revealed inadequacies in meeting the concurrent demands of large-scale deployment and high-bandwidth requirements. Addressing this challenge, we propose a distributed CBC system architecture based on the optimized stochastic parallel gradient descent (SPGD) algorithm. Our strategy segments the large-scale laser array into multiple independent smaller-scale subarrays, ensuring their efficient phase convergence through the introduction of corresponding reference lasers while avoiding interference when integrating different subarrays. Moreover, the piecewise SPGD algorithm is proposed and the intensity of the reference laser is modulated to further improve the convergence speed and accuracy within subarrays, enhancing the algorithm's compatibility across laser arrays of varying scales. We have validated the feasibility of the distributed CBC architecture through numerical analysis and assessed the strategy's performance in both static and dynamic environments using simulation software. The simulation findings indicate that, compared to traditional CBC systems, distributed architecture with 3, 7, and 19 subarrays and utilizing the piecewise SPGD algorithm, has experienced phase control bandwidth enhancements by factors of approximately 3.6, 10.4, and 32.5 respectively, maintaining superior average power output in dynamic noise environments. The proposed architecture and strategy also accommodate subarrays of variable scales and obviates the necessity for large-aperture optical components on the emitted plane, demonstrating exceptional scalability and adaptability.

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Dynamic beam shaping with a compact coherently combined fiber laser array

Su,

Long,

Jin

et al. 2024

Chin. Opt. Lett.

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Non-mechanical axial focus tuning by coherent beam combining technique

Cited by 3 publications

References 34 publications

Single-step phase identification and phase locking for coherent beam combination using deep learning

Single-step phase identification and phase locking for coherent beam combination using deep learning

Efficient distributed architecture and optimized subarray control strategy to facilitate large-scale coherent beam combination

Dynamic beam shaping with a compact coherently combined fiber laser array

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