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Wafer Defect Detection with YOLO by Jayanthi Adigarla is a document available to read on EtoBox.

This document discusses the use of deep learning techniques, specifically the YOLO architecture, for the localization and classification of wafer defects in semiconductor manufacturing. The study demonstrates that YOLOv3 and YOLOv4 achieve over 94% classification accuracy in real-time, outperforming other models like ResNet50 and DenseNet121, which lack localization capabilities. The findings highlight the importance of automated defect detection to improve manufacturing efficiency and quality management.

Author

Jayanthi Adigarla

Language

EN