深度学习用于在连贯光束组合中同时识别相位和振幅
Fedor Chernikov1, Yunhui Xie2, James A Grant-Jacob2
1Optoelectronics Research Centre, University of Southampton, Southampton, UK. fc1e21@soton.ac.uk.
Scientific reports
|April 6, 2025
概括
一个新的深度学习算法可以同时识别光纤激光束组合的相位和振幅,即使功率波动. 这提升了高功率激光系统的相锁定.
科学领域:
- 光学和光子学 在光学和光子学.
- 激光物理 激光物理
- 人工智能的人工智能
背景情况:
- 连贯光束组合 (CBC) 增强了光纤激光器的功率,但需要精确的相位控制.
- 传统的相位检索算法与运行光纤激光器中常见的功率波动作斗争.
- 现有的方法通常假定稳定的功率水平,限制了实际的CBC应用.
研究的目的:
- 为CBC系统开发一个强大的相位检索方法,以适应功率波动.
- 为了能够从联合光束强度直接同时识别相位和振幅.
- 评估拟议的深度学习方法的性能和可扩展性.
主要方法:
- 设计了一个深度学习算法,用于单步相位和振幅检索.
- 该算法从单个摄像头对联合光束的观察中分析强度模式.
- 使用空间光调节器的模拟研究了功率波动效应和可扩展性,随着光束的增加而增加.
主要成果:
- 深度学习算法准确地解开相位和功率,即使有显著的功率波动.
- 系统调查证实了功率水平波动对相位检索精度的影响.
- 可扩展性分析表明,该方法能够在更多的光束中保持精度.
结论:
- 深度学习为CBC系统中面临功率降低的相位和振幅检索提供了强大的解决方案.
- 提出的方法克服了在非理想功率条件下传统算法的局限性.
- 这种方法提高了高功率光纤激光系统的实用性和可扩展性.
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