• Chinese Journal of Lasers
  • Vol. 50, Issue 11, 1101010 (2023)
Zhiqaing Gao1, Qi Chang1, Haoyu Liu1, Jun Li1、2、3, Pengfei Ma1、2、3、*, and Pu Zhou1
Author Affiliations
  • 1College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, Hunan, China
  • 2Nanhu Laser Laboratory, National University of Defense Technology, Changsha 410073, Hunan, China
  • 3Hunan Provincial Key Laboratory of High Energy Laser Technology, Changsha 410073, Hunan, China
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    DOI: 10.3788/CJL230656 Cite this Article Set citation alerts
    Zhiqaing Gao, Qi Chang, Haoyu Liu, Jun Li, Pengfei Ma, Pu Zhou. Research Progress and Development Trend of Machine Learning in Phase Control of Fiber Laser Arrays[J]. Chinese Journal of Lasers, 2023, 50(11): 1101010 Copy Citation Text show less



    Phase control is a key factor in achieving coherent beam combining. Recently, the number of coherent combining paths has been continuously expanding, and the achieved combining power has been continuously increasing. However, when the power of a single combining light source exceeds kilowatts or even several kilowatts, the residual of the phase-locked control system significantly increases with the complexity of the application environment. With the rapid development of artificial intelligence technology, exploring new phase control methods based on machine learning has become a new development trend.


    In 2019, Tünnermann et al. introduced reinforcement learning into coherent combining systems, achieving the prediction and compensation of phase noise below kHz (Fig. 1). In 2021, the team validated the feasibility of applying the reinforcement learning phase-locked control method to tiled-aperture coherent combining systems in a simulation environment and explored the ability of the control method to achieve combining light field shaping (Fig. 2). To overcome the limitations of reinforcement learning in expanding the number of coherent combining units, in 2021, Shpakovych et al. proposed a two-dimensional phase dynamic control scheme based on neural networks. This scheme uses a quasi-reinforcement learning method based on neural networks, and the phase-locked residual can reach up to λ/30 (Fig. 3). In 2022, Shpakovych et al. implemented the phase control of a seven-channel fiber amplifier array using a quasi-reinforcement learning algorithm (Fig. 4).

    To test the feasibility of phase locking using deep learning in energy-type fiber laser coherent combining systems, in 2019, Hou et al. introduced deep learning into coherent combining systems for the first time and achieved phase locking (Fig. 6). Subsequently, the Chinese Academy of Sciences in China, the Berkeley National Laboratory in the United States, and the University of Southampton in the United Kingdom conducted the concept or experimental verification of phase-locking based on deep learning.

    In addition to energy-based applications, the large array element characteristics and ability to quickly adjust the sub-beam phase of coherent combining systems provide a novel technical approach for the generation and customization of special light fields with high power and high mode switching speed. To solve the failure of light field control caused by phase conjugation, Hou et al. proposed the concept of phase-locked control evaluation function based on non-focal plane extraction. Further, they extended the evaluation function of power in the bucket widely used in the study of energy concentrated spot generated by conventional coherent combining to a generalized evaluation function suitable for complex light field customization, achieving decoupling control of the laser array conjugation phase. The feasibility of generating complex light fields such as orbital angular momentum beams was demonstrated. In 2020, Chang et al. proposed the problem of phase conjugation decoupling in the generation of coherent array special light fields using scatterers. With the application of artificial intelligence algorithms in energy-based coherent combining systems, introducing them into the array light field control of special beams to address complex phase control problems has become a new research approach.

    In 2020, Hou et al. introduced deep learning algorithms into fiber laser arrays to achieve optical field regulation through a two-step phase control (Fig. 11). To further investigate the optical field information, in 2022, Hou et al. customized orbital angular momentum (OAM) beams from an angle domain perspective and introduced deep learning algorithms to learn the mapping of the relative phase from intensity information to array unit beams (Fig. 12). The purity of the OAM mode in the later stages of phase control using angular domain information has been improved, verifying that angular domain light field information is helpful to control the phase accurately.

    Conclusions and prospects

    Currently, significant results have been achieved in the design and preliminary verification of improving the phase control capability of fiber laser arrays based on machine learning. The number of control paths has exceeded 100, and it demonstrates better performance than traditional optimization algorithms in terms of control speed and control accuracy. However, issues still exist to be addressed in performance verification under high-power medium/strong noise conditions, and the system verification of larger array elements is urgently required. With the development of artificial intelligence technology, the comprehensive improvement in the sample training speed, capacity, accuracy, and mining accuracy is expected to promote machine learning to play a greater potential role in array laser phase regulation.

    Zhiqaing Gao, Qi Chang, Haoyu Liu, Jun Li, Pengfei Ma, Pu Zhou. Research Progress and Development Trend of Machine Learning in Phase Control of Fiber Laser Arrays[J]. Chinese Journal of Lasers, 2023, 50(11): 1101010
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