Machine Learning Based Automatic Mode-Locking of a Dual-Wavelength Soliton Fiber Laser

Abstract

Recent years have witnessed growing research interest in dual-wavelength mode-locked fiber lasers for their pivotal role in diverse applications and the exploration of nonlinear dynamics. Despite notable progress in their development, achieving reliable mode-locked dual-wavelength operation typically necessitates intricate manual adjustments of the cavity’s polarization components. In this article, we present the realization of automatic mode-locking in a dual-wavelength soliton fiber laser. To provide guidance for the algorithm design, we systematically investigated the impact of polarization configurations and initial states on the laser’s operation through numerical simulations and linear scan experiments. The results indicate that operational regimes can be finely adjusted around the wave plate position supporting the mode-locked dual-wavelength solution. Furthermore, the laser exhibits multiple stable states at the mode-locked dual-wavelength point, with critical dependence on the initial conditions. Accordingly, we developed a two-stage genetic algorithm that was demonstrated to be effective for realizing automatic dual-wavelength mode-locking. To further improve the performance of the algorithm, a feedforward neural network was trained and integrated into the algorithm, enabling accurate identification of the dual-wavelength states. This study provides valuable insights into understanding how polarization configurations and initial conditions impact the operational regimes of dual-wavelength mode-locked fiber lasers. The algorithm developed can be extended to optimize other systems with multiple stable states supported at the same parameter point.

Keywords

• machine learning
• genetic algorithm
• soliton
• dual-wavelength mode-locking
• fiber laser