Non-destructive Evaluation of the Nanosecond Laser Damage Characteristics of the Nd,Y:CaF2 Crystal Via Photothermal Weak Absorption Using a Fully Connected Neural Network

Nd,Y:CaF2 (NYCF) crystals are exceptional gain materials for high-power laser drivers; however, laser-induced damage remains a substantial challenge that restricts their broader application. In this study, by establishing an in situ testing system for photothermal weak absorption and the laser-induced damage threshold (LIDT), the relationship between the photothermal weak absorption characteristics of NYCF and its LIDTs was analyzed. A fully connected neural network was employed to facilitate deep learning of these relationships, thereby enabling non-destructive evaluation of NYCF via photothermal weak absorption. Moreover, this study examined both the effect of spot size during testing and the influence of crystal orientation on the evaluation outcomes. The underlying mechanisms were further elucidated by investigating NYCF's thermal mechanical properties and damage characteristics. This work not only offers a rapid, non-destructive method for evaluating the laser damage resistance of NYCF using artificial intelligence but also enhances the understanding of its damage mechanisms.