Saliency-Based Defect Detection in Industrial Images by Using Phase Spectrum

Abstract

For computer vision-based inspection of electronic chips or dies in semiconductor production lines, we propose a new method to effectively and efficiently detect defects in images. Different from the traditional methods that compare the image of each test chip or die with the template image one by one, which are sensitive to misalignment between the test and template images, a collection of multiple test images are used as the input image for processing simultaneously in our method with two steps. The first step is to obtain salient regions of the whole collection of test images, and the second step is to evaluate local discrepancy between salient regions in test images and the corresponding regions in the defect-free template image. To be more specific, in the first step of our method, phase-only Fourier transform (POFT), which is computationally efficient for online applications in industry, is used for saliency detection. We provide the theoretical justification for POFT to be effective to attenuate the normal regions and amplify the defects in multiple test images, which are usually arranged in a matrix format in industrial practice. By comparing with four other popular methods, the proposed algorithm can efficiently accommodate small variations (inevitable in practice) in test chips or dies, such as the spatial misalignments and product variations. Experimental results on a large-scale database including 1073 images, 94 of which are defective, show that our method performs much better than the other methods in terms of precision, recall, and F-measure.