Self assured Deep Learning with Minimum Pre Labeled Data for Wafer Pattern Classification

• Published in: IEEE transactions on semiconductor manufacturing Vol. 36; no. 3; p. 1
• Main Authors: Fan, Shu-Kai S., Tsai, Du-Ming, Shih, Ya-Fang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Artificial neural networks; Convolutional neural networks; Data models; Deep learning; Defects; Labeling; Labels; Machine learning; minimum pre-labeled data; Neural networks; Pattern classification; Pattern recognition; pseudo labeling; self-assured labeling; Semiconductor device modeling; Subject specialists; Supervised learning; Training; wafer defect pattern classification

Data quality plays an important role during the training stage of machine/deep learning models. The annotation hinges on the experiences of domain experts. To acquire the experts knowledge in the context of machine learning, manual data labeling, a tedious and time-consuming task in supervised learn...