Red-Diode Pumped 1.2-μm Holmium-Doped Fluoride Fiber Lasers: Spectroscopic Analysis and Performance Optimization

In this work, we present an in-depth analysis of a Ho<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3+</sup>-doped ZrF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> fiber laser operating at 1.2 μm, pumped using a 640 nm laser diode. First, we present spectroscopic studies on fiber emission under red excitation, discussing and interpreting a series of emission lines attributed to Stokes and anti-Stokes transitions, with the latter involving up-conversion processes. We analyze their impact on the laser's operating characteristics. Next, we report on the laser built from the same fiber, operating at 1.2 μm with an output power of approximately 60 mW and a slope efficiency slightly above 25%. Further, using experimental data and spectroscopic parameters, we develop a mathematical model that incorporates holmium ion population rate equations alongside pump and signal power propagation equations. The agreement between theoretical predictions and experimental results confirms the model's validity. In the final part of this study, we implement a genetic algorithm to optimize the laser's operating parameters, including pumping efficiency, reabsorption, fiber length, and mirror parameters. In particular, we propose a double-clad fiber structure that significantly reduces excited-state absorption of the pump. Finally, by globally optimizing the laser's operating conditions, we numerically demonstrate that an output power of up to 18.7 W and a slope efficiency exceeding 31% are achievable. The presented results demonstrate a promising semi-empirical approach for optimizing laser performance parameters. <list list-type="simple" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <list-item> 1.2-μm fiber lasers, red-diode pumping, holmium lasers, fluorozirconate glass fibers, genetic algorithms. </list-item> </list>