Artificial Intelligence-Enabled Mode-Locked Fiber Laser: A Review

Abstract

Owing to their compactness, robustness, low cost, high stability, and diffraction-limited beam quality, mode-locked fiber lasers play an indispensable role in micro/nanomanufacturing, precision metrology, laser spectroscopy, LiDAR, biomedical imaging, optical communication, and soliton physics. Mode-locked fiber lasers are a highly complex nonlinear optical system, and understanding the underlying physical mechanisms or the flexible manipulation of ultrafast laser output is challenging. The traditional research paradigm often relies on known physical models, sophisticated numerical calculations, and exploratory experimental attempts. However, when dealing with several complex issues, these traditional approaches often face limitations and struggles in finding effective solutions. As an emerging data-driven analysis and processing technology, artificial intelligence (AI) has brought new insights into the development of mode-locked fiber lasers. This review highlights the areas where AI exhibits potential in accelerating the development of mode-locked fiber lasers, including nonlinear dynamics prediction, ultrashort pulse characterization, inverse design, and automatic control of mode-locked fiber lasers. Furthermore, the challenges and potential future development are discussed.

Methods Controlling Radiation Parameters of Mode-Locked All-Fiberized Lasers

“…Here, the output data are radiation parameters (in the case of short pulses, it will be spectrum, duration, energy, and pulse structure), whereas the input data are the pumping radiation power and other control parameters described earlier. The objective of intellectual technologies is the identification of complex dependencies between the input and output data [83][84][85][86][87][88][89][90].…”