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Quantitative correlation between surface defect dimensions and absorption in optical components
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AI
DOI：10.1364/AO.574764.png)
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Abstract
En 中文
High-quality optical components constitute a fundamental assurance for the stable operation of high-precision optical systems, with surface defects existing as a critical limiting factor in the improvement of their laser-induced damage resistance performance. Surface defects in optical components of high-energy laser systems induce localized thermal deposition through their absorbing properties, leading to component failure and compromised system integrity. Therefore, comprehensive characterization of defect absorption features becomes critically essential. The surface thermal lens (STL) technique has emerged as a prevalent method for determining the absorption characteristics of optical components, owing to its heightened sensitivity and simplified optical configuration. This study systematically investigates the quantitative correlation between absorption characteristic of surface defects and their dimensional parameters in optical components through simulation and experiment. The simulation results demonstrate that increasing defect dimension leads to aggravated temperature rise in localized regions, corresponding to an enhanced surface thermal lensing signal and heightened absorption of the pump laser in the defect area. A surface thermal lensing absorption detection platform was constructed, and Vickers indentations with varied dimensions were fabricated on a K9 optical component to simulate defects, enabling the acquisition of the corresponding surface thermal lensing signal. The experimental results verify a positive correlation between the measured photothermal signal and defect dimension. The absorption characteristics of surface defects in optical components were quantitatively characterized through curve fitting, offering a novel methodology, to our knowledge, for surface quality evaluation. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
Keywords:
THIN-FILM COATINGS
THERMAL LENS
FUSED-SILICA
SENSITIVITY
DEFLECTION