YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly
  Patterns: A Data-Centric Approach
Enrique Dehaerne Bappaditya Dey Hossein Esfandiar Lander Verstraete Hyo Seon Suh Sandip Halder Stefan De Gendt
Shrinking pattern dimensions leads to an increased variety of defect types in
semiconductor devices. This has spurred innovation in patterning approaches
such as Directed self-assembly (DSA) for which no traditional, automatic defect
inspection software exists. Machine Learning-based SEM image analysis has
become an increasingly popular research topic for defect inspection with
supervised ML models often showing the best performance. However, little
research has been done on obtaining a dataset with high-quality labels for
these supervised models. In this work, we propose a method for obtaining
coherent and complete labels for a dataset of hexagonal contact hole DSA
patterns while requiring minimal quality control effort from a DSA expert. We
show that YOLOv8, a state-of-the-art neural network, achieves defect detection
precisions of more than 0.9 mAP on our final dataset which best reflects DSA
expert defect labeling expectations. We discuss the strengths and limitations
of our proposed labeling approach and suggest directions for future work in
data-centric ML-based defect inspection.