The semiconductor manufacturing industry relies heavily on wafer surface defect detection for yield enhancement. Machine learning and digital image processing technologies have been used in the development of various detection algorithms. However, most wafer surface inspection algorithms are not be applied in industrial environments due to the difficulty in obtaining training samples, high computational requirements, and poor generalization. In order to overcome these difficulties, this paper introduces a full-flow inspection method based on machine vision to detect wafer surface defects. Starting with the die image segmentation stage, where a die segmentation algorithm based on candidate frame fitting and coordinate interpolation is proposed for die sample missing matching segmentation. The method can segment all the dies in the wafer, avoiding the problem of missing dies splitting. After that, in the defect detection stage, we propose a die defect anomaly detection method based on defect feature clustering by region, which can reduce the impact of noise in other regions when extracting defect features in a single region. The experiments show that the proposed inspection method can precisely position and segment die images, and find defective dies with an accuracy of more than 97%. The defect detection method proposed in this paper can be applied to inspect wafer manufacturing.
Keywords: feature extraction; image segmentation; machine learning; machine vision; wafer surface defect detection.