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首页> 外文会议>Machine Vision Applications in Industrial Inspection XIV; Electronic Imaging Science and Technology >Simultaneous Photometric Correction and Defect Detection in Semiconductor Manufacturing
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Simultaneous Photometric Correction and Defect Detection in Semiconductor Manufacturing

机译：半导体制造中的同时光度校正和缺陷检测

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This paper reports on an image processing algorithm for simultaneous photometric correction and defect detection in semiconductor manufacturing. We note that this problem has some resemblance to change detection in real time image analysis. In particular, the changes between the two images are analogous to the defects in our machine vision system. We therefore applied several detection methods and examined their applicability to defect detection. We first performed a sub-pixel image registration, using a phase correlation method together with a singular value decomposition factorization of the correlation matrix to compute the necessary alignment. We then tested a few change detection methods, including the shading model, derivative model, statistical change detection, linear dependence change detector and Wronskian change detection model. We subjected this system to our collection of raw data acquired from an industrial system, and we evaluated the different methods with respect to the detection accuracy, robustness, and speed of the system. We have promising results at this stage, especially in detecting the blob and line defects that are most commonly found, and when the lighting variation is within a certain threshold.
机译：本文报告了一种用于半导体制造中同时进行光度校正和缺陷检测的图像处理算法。我们注意到，这个问题与实时图像分析中的变化检测有些相似。特别地，两个图像之间的变化类似于我们的机器视觉系统中的缺陷。因此，我们应用了几种检测方法，并检查了它们在缺陷检测中的适用性。我们首先使用相位相关方法以及相关矩阵的奇异值分解分解来执行子像素图像配准，以计算必要的对齐方式。然后，我们测试了几种变化检测方法，包括阴影模型，导数模型，统计变化检测，线性相关变化检测器和Wronskian变化检测模型。我们对该系统进行了从工业系统中获取的原始数据的收集，并且就系统的检测精度，鲁棒性和速度方面评估了不同的方法。在此阶段，我们取得了令人鼓舞的结果，尤其是在检测到最常见的斑点和线条缺陷以及照明变化在某个阈值内时。

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