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Simultaneous photometric correction and defect detection in semiconductor manufacturing

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.