A Novel Defect Diagnosis Method for Kyropoulos Process Based Sapphire Growth
Wei Zhang (Taiyuan University of Technology)
Tiezhu Qiao (Taiyuan University of Technology)
Y Pang (TU Delft - Transport Engineering and Logistics)
Yi Yang (Taiyuan University of Technology)
Hong Chen (Shanxi Zhongjujingke Semiconductor Co.)
Guirong Hao (Shanxi Zhongjujingke Semiconductor Co.)
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Abstract
When sapphire crystal is prepared with Kyropoulos method, the necking-down growth process is a key stage. Sapphire growth defect is a big problem in this stage. However, diagnosing growth defects is subject to the interference of workers subjectivity and accuracy always goes down. To address the problem, a novel defect diagnosis method is proposed for necking-down growth process in this paper. Industrial CCD sensors replace eyes of skilled workers to observe in this method. A new Defect-Diagnosing Siamese network (DDSN) is used in this method. We use Siamese architecture to learn similarity through pairs of images. We use the deep separable convolution (DSC) into the DDSN to optimize running speed and model size. In experiment, dataset is acquired by industrial CCD sensors in the necking-down growth process. The accuracy of defect diagnosis can reach up to 94.5%. The method significantly improves the traditional way.