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In semiconductor manufacturing, the detection of defects efficiently and accurately plays an important role in improving production quality and process optimization. However, most of the current defect inspection methods need to collect reference images on wafer. Based on the machine learning (ML) model, this paper first using the layout to generate the corresponding Scanning Electron Microscopy (SEM) image as the reference image for defect, and then by comparing the similarity of the defect image with the generated reference image to achieve accurate identification and localization of the defect. Experimental results demonstrate that the accuracy of this method for defect inspection is 98%, at the same time, the processing speed is at 100 image levels per minute. This study can not only improve the accuracy and efficiency of defect inspection, but also provide new ideas for the processing and multi-classification of defect images.
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