Defect detection of glassivation passivation parts wafer surface with random texture and different brightness

Abstract

Automatic defect detection for glassivation passivation parts (GPP) wafer surface becomes an extremely challenging task, due to the interference of random texture, disturbance of low-contrast pseudo defects within the image, and difference of different brightness between images. In this paper, we propose a novel defect detection scheme for GPP wafer surface with random texture and different brightness. First, an automatic segmentation method center expansion idea-based for the region of interest (ROI) of die image is presented to eliminate the interference from edge background and improve the efficiency of defect detection. Then, a feature point set extraction method based on adaptive gain and error diffusion is proposed so that the defect feature between images with different brightness can be highlighted by adaptive gain, and the candidate defect feature point set under random texture feature is able to be extracted by error diffusion. Finally, the density-based spatial clustering of applications with noise considering grayscale constraint (GC-DBSCAN) is designed to identify the true defect clusters from the candidate defect feature point set and accomplish the detection of various types of surface defects. Experimental results show that the proposed method can completely implement the extraction of crack and dirty defects and eliminate the false detection caused by random texture and different brightness, which is very efficient and superior to other methods.