Dark-field surface defects detection method for multi-surface-shape large aperture optical components

被引:0
|
作者
Guo, Shiwei [1 ,2 ]
Wang, Shiling [3 ]
Wang, Shaowen [1 ,2 ]
Wu, Lan [1 ,2 ]
Liu, Dong [1 ,2 ]
机构
[1] Zhejiang Univ, Coll Opt Sci & Engn, State Key Lab Extreme Photon & Instrumentat, Hangzhou 310027, Zhejiang, Peoples R China
[2] Zhejiang Univ, ZJU Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou 311215, Peoples R China
[3] China Jiliang Univ, Coll Metrol & Measurement Engn, Hangzhou 310018, Peoples R China
关键词
REAL-TIME; DAMAGE; SCATTERING; MICROSCOPY;
D O I
10.1364/AO.531320
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
In large-scale high-power optical systems such as inertial confinement fusion systems, the design of various optical components is often larger and more complex. Therefore, determining how to ensure the quality evaluation of optical components faces new challenges. As a key evaluation step for component quality, surface defects detection needs to consider improving the detection capability for various complex surface shapes and large aperture components. Meanwhile, the accuracy level of detection does not decrease with an increase in detection aperture size. The defects that need to be detected are typically small in size and randomly distributed throughout the aperture. Comprehensive aperture-wide information is required to ensure the thorough detection of defects in the components. Therefore, it is required that the detection system maintains compatibility with multi-surface shapes while balancing detection efficiency and accuracy. Against this background, the surface defects detection technology with high compatibility is explored in this paper. The illumination system of the dark-field imaging system and a generalized scanning path search strategy is proposed. Under the condition of ensuring a detection sensitivity of 0.5 mu m, surface defects detection for various types of optical components with apertures several hundred times larger than the detection field of view is achieved. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
引用
收藏
页码:6686 / 6695
页数:10
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