Accurate analysis of nanoscale defects in semiconductor manufacturing is essential for optimizing yield and reliability. Existing methods heavily rely on large, labor-intensive datasets and primarily focus on macroscopic defect distributions rather than finer nanoscale defect morphology. In this study, we introduce a novel hybrid weakly supervised segmentation framework for scanning electron microscope (SEM) images, which significantly reduces labeling demands while maintaining high precision. Our approach consists of two interconnected subnetworks: the first is dedicated to precise defect localization and image cropping, and the second performs detailed segmentation of the localized regions. Additionally, we propose an enhanced H-WSSNet that employs Leaky ReLU and a novel multi-level feature fusion mechanism, addressing gradient vanishing during training and improving the model’s adaptive feature fusion and selection capabilities. Extensive validation on a dataset of 1,328 real-world SEM images shows that our model achieves accuracy comparable to fully supervised methods, but with only 10% of the labeling workload. This advancement opens up new possibilities for efficient and scalable high-precision defect segmentation in semiconductor manufacturing.

In semiconductor manufacturing, pinpointing nanoscale wafer defects is crucial for yield and reliability. Deep learning methods for defect segmentation rely heavily on large, labor-intensive datasets and focus mainly on macroscopic wafer defects, not nanoscale morphology. Our research introduces a hybrid weakly supervised scanning electron microscope (SEM) defect segmentation system with two sub-networks: one for accurate defect localization and image cropping, another for detailed segmentation. Validated on 1,328 SEM image defects from a real facility, our model surpasses existing weakly supervised methods and equals fully supervised models in accuracy, with 10% labeling effort, providing a novel approach for high-precision defect segmentation.