Observational and Experimental Insights into Machine Learning-Based Defect Classification in Wafers

arXiv:2310.10705v4 Announce Type: replace
Abstract: This survey paper offers a comprehensive review of methodologies utilizing machine learning (ML) classification techniques for identifying wafer defects in semiconductor manufacturing. Despite the growing body of research demonstrating the effectiveness of ML in wafer defect identification, there is a noticeable absence of comprehensive reviews on this subject. This survey attempts to fill this void by amalgamating available literature and providing an in-depth analysis of the advantages, limitations, and potential applications of various ML classification …

abstract arxiv classification cs.ai cs.lg defects experimental identification insights machine machine learning manufacturing paper research review semiconductor semiconductor manufacturing survey type