Machine learning-driven thermal management and output behavior prediction for a multi-segment bonded Tm:YAG laser.

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This paper addresses the thermal instability of lasers resulting from the thermal effects of the 2 µm gain medium, proposing what we believe to be a novel compensation scheme that integrates machine learning technology with multi-segment bonded Tm: YAG crystals and negative lenses, based on the thermal focal length model of a thick thermal lens. This approach significantly optimizes thermal compensation and facilitates rapid assessment of the light-emitting behavior trends of Tm: YAG lasers. Firstly, the thermal behavior of conventional and multi-segment bonded Tm: YAG crystals is analyzed. An apparently new thermal focal length model for thick lenses is established based on thin lens theory, and BP neural networks are employed to screen and predict the performance of both models. It demonstrates superior predictive capability at specific power levels, achieving a maximum error of 1.8 mm and a minimum error rate of 1.9%. Following this, we select negative lenses with varying focal lengths for thermal compensation experiments, revealing that the compensation effects differ based on the focal lengths and positions of the negative lenses at varying pump powers. To address this complex nonlinear relationship, we utilize a random forest optimization algorithm, which successfully predicts the impact of negative lens positioning on output power across three different cavity lengths, resulting in prediction errors of 1.4%, 1.1%, and 2.1%. The model performs particularly well when the Tm: YAG laser approaches destabilization. This high-accuracy predictive model enables rapid identification of the optimal position for the negative lens, facilitating effective thermal compensation while streamlining traditional numerical simulation processes. Moreover, it provides critical guidance for the thermal management of 2 µm lasers and enhances the precision of assessments related to their light-emitting behavior.