红外与激光工程, 2019, 48 (11): 1113003, 网络出版: 2019-12-09   

超光滑表面缺陷的分类检测研究

Study on classification and detection of supersmooth surface defects
作者单位
1 西安工业大学 陕西省薄膜技术与光学检测重点实验室, 陕西 西安 710021
2 中国兵器科学院宁波分院, 浙江 宁波 310022
摘要
为了区分超光滑表面上方存在的微小粒子、亚表面缺陷、微粗糙度三种缺陷产生的散射光, 并得到能够探测这三种缺陷的最佳区域, 将双向反射分布函数(Bidirectional Reflection Distribution Function, BRDF)与琼斯矩阵结合, 给出了三种缺陷在ss、sp、ps、pp四种偏振状态下的偏振系数。在此基础上, 模拟和分析了三种缺陷在四种偏振状态下与散射方位角的关系。结果表明: 利用p偏振入射光引起的p偏振散射光能将这几种缺陷区分开。根据三种缺陷与散射方位角变化关系的不同, 给出了三种缺陷的最佳探测区域及实现方法。
Abstract
In order to distinguish the scattered light generated by the three defects of micro-particles, sub-surface and micro-roughness existing above the super smooth surface, and to obtain the best region for detecting these three scattering mechanisms, combined the Bidirectional Reflectance Distribution Function (BRDF) with Jones matrix and the polarization coefficients of the three defects were given in the four polarization states ss, sp, ps, pp. On this basis, the relationship between the three defects and scattering azimuth in four polarization states was simulated and analyzed. The results show that these defects could be distinguished by using p-polarized scattering light induced by p-polarized incident light. According to the different relations between the three defects and the variation of scattering azimuth, the best area to distinguish three kinds of defects and its realization methods were given.

解格飒, 王红军, 王大森, 田爱玲, 刘丙才, 朱学亮, 刘卫国. 超光滑表面缺陷的分类检测研究[J]. 红外与激光工程, 2019, 48(11): 1113003. Xie Gesa, Wang Hongjun, Wang Dasen, Tian Ailing, Liu Bingcai, Zhu Xueliang, Liu Weiguo. Study on classification and detection of supersmooth surface defects[J]. Infrared and Laser Engineering, 2019, 48(11): 1113003.

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