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.

Identifying the root cause of defects in semiconductor manufacturing is crucial for enhancing product yield and reliability. Traditional methods often emphasize defect classification and detection, yet they lack the depth required for comprehensive root cause analysis. This study introduces DefectTrackNet, a pioneering framework designed for automatic root cause analysis through historical similarity image retrieval. Our system employs a novel hybrid CNNTransformer architecture, which excels in the precise extraction and matching of image features pertinent to defect origins. Validated on a dataset of 2,588 real SEM defect images annotated with root causes, DefectTrackNet demonstrates superior performance in both accuracy and retrieval speed compared to existing methodologies. This innovative approach not only offers significant improvements over conventional techniques but also establishes a new benchmark for efficient and accurate defect root cause analysis, thereby advancing the field of semiconductor manufacturing defect analysis.

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.