A fast oxidation region detection algorithm based on differential geometry approach for high-density flexible integrated circuit packaging substrates

Vision inspection has been widely used in the field of defect measurement as a noncontact, nondestructive measurement technique. Conventional visual defect detection methods rely heavily on standard templates and image features, but the standard templates are either difficult to obtain or do not exist in many application fields, especially in flexible integrated circuit (IC) substrate defect detection at a microscopic level. To solve the above problems, a rapid online oxidation defect detection algorithm for micron-level flexible IC substrate oxidation defects, based on the differential geometric approach, is proposed in this paper. First, the sample fitting model is established by using minimal generalized variance as well as the surface fitting model. Second, the defect area of the substrate is determined by analyzing the similarity and membership, which is coded among the pixels by considering the spatial topological structure of the image. Finally, experiments are performed to show that the algorithm proposed in this paper not only has high precision, but also meets industrial real-time requirements for the rapid production of a flexible substrate. Since the method does not depend on the standard template of the original image, it can achieve vision detection effects at high speed and with high precision.