Defect inspection in semiconductor images using FAST-MCD method and neural network

Most defect inspection methods used in semiconductor manufacturing require design layout or golden die images. Unlike methods that require such additional information, this paper presents a method for automatic inspection of defects in semiconductor images with a single image. First, we devise a method to classify images into four types: flat, linear, patterned, and complex using a cosine similarity. For linear and patterned images, we obtain defect-free images that retain the structure. A flat image is then obtained by subtracting the defect-free image from the input image. The FAST-MCD method then estimates the parameters of the inlier distribution of the flat image and uses them to detect defects. A segmentation neural network is used to detect defects in complex images. Unlike conventional methods that only work on a specific structure, our method classifies structures and finds defects in each structure. We use 16 defective images in our experiments, where our method detects all 16 defective images, while the conventional methods detect fewer defective images.