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.

翻译：随着图案尺寸的不断缩小，半导体器件中的缺陷类型日益增多。这推动了定向自组装（DSA）等图案化方法的创新，但目前尚无传统自动缺陷检测软件可供使用。基于机器学习的扫描电镜图像分析已成为缺陷检测领域日益热门的研究课题，其中监督式机器学习模型通常表现出最佳性能。然而，关于为这些监督模型获取高质量标注数据集的研究却很少。本文提出了一种方法，用于为六边形接触孔DSA图案数据集获取连贯且完整的标注，同时将DSA专家的质量把控工作量降至最低。研究表明，最先进的神经网络YOLOv8在我们最终数据集上的缺陷检测精度超过0.9 mAP，该数据集最能反映DSA专家对缺陷标注的期望。我们讨论了所提标注方法的优势与局限性，并为未来数据中心机器学习缺陷检测研究指明了方向。