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Multistability manipulation by reinforcement learning algorithm inside mode-locked fiber laser
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DOI：10.1515/nanoph-2023-0792.png)
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Fiber mode-locked lasers are nonlinear optical systems that provide ultrashort pulses at high repetition rates. However, adjusting the cavity parameters is often a challenging task due to the intrinsic multistability of a laser system. Depending on the adjustment of the cavity parameters, the optical output may vary significantly, including Q-switching, single and multipulse, and harmonic mode-locked regimes. In this study, we demonstrate an experimental implementation of the Soft Actor-Critic algorithm for generating a harmonic mode-locked regime inside a state-of-the-art fiber laser with an ion-gated nanotube saturable absorber. The algorithm employs nontrivial strategies to achieve a guaranteed harmonic mode-locked regime with the highest order by effectively managing the pumping power of a laser system and the nonlinear transmission of a nanotube absorber. Our results demonstrate a robust and feasible machine-learning-based approach toward an automatic system for adjusting nonlinear optical systems with the presence of multistability phenomena.
Keyword:
multistability
harmonic mode-locked lasers
reinforcement learning
single wall carbon nanotubes
saturable absorber
期刊
IF:
6.6
论文数:
3.0K
被引数:
1.6W
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2 GHz passively harmonic mode-locked fiber laser by a microfiber-based topological insulator saturable absorber.png)
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