用合成数据训练模型，实现极小缺陷的高精度实时检测。
Defect Detection in Photolithographic Patterns Using Deep Learning Models Trained on Synthetic Data
- 通过生成带标注的SEM图像解决真实缺陷数据不足问题
- YOLOv8检测小至0.15μm的缺陷，平均精度达96%
- 适合半导体制造中缺陷检测的快速部署与模型验证
在极紫外光刻（EUV）图案化过程中，因图形尺寸不断缩小，各类缺陷变得极为微小，导致检测中出现误报或漏检。由于缺乏带有缺陷标注且能充分代表小缺陷的真实质量数据，基于深度学习的缺陷检测模型难以在生产线部署。为此，本文人工生成具有已知缺陷分布的线状图案扫描电子显微镜（SEM）图像，并自动标注。采用先进目标检测模型评估不同缺陷尺寸下的检测性能，结果表明，实时检测器YOLOv8的平均精度达96%，优于EfficientNet的83%和SSD的77%，可有效检测小于节距宽度的缺陷。实验显示，该模型在真实SEM数据上对桥接缺陷的检测准确率为84.6%，断线缺陷为78.3%。结果表明，合成数据可作为真实数据的有效替代，用于构建鲁棒的机器学习模型。
原文摘要 · Abstract (English)
In the photolithographic process vital to semiconductor manufacturing, various types of defects appear during EUV pattering. Due to ever-shrinking pattern size, these defects are extremely small and cause false or missed detection during inspection. Specifically, the lack of defect-annotated quality data with good representation of smaller defects has prohibited deployment of deep learning based defect detection models in fabrication lines. To resolve the problem of data unavailability, we artificially generate scanning electron microscopy (SEM) images of line patterns with known distribution of defects and autonomously annotate them. We then employ state-of-the-art object detection models to investigate defect detection performance as a function of defect size, much smaller than the pitch width. We find that the real-time object detector YOLOv8 has the best mean average precision of 96% as compared to EfficientNet, 83%, and SSD, 77%, with the ability to detect smaller defects. We report the smallest defect size that can be detected reliably. When tested on real SEM data, the YOLOv8 model correctly detected 84.6% of Bridge defects and 78.3% of Break defects across all relevant instances. These promising results suggest that synthetic data can be used as an alternative to real-world data in order to develop robust machine-learning models.
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