Presentation
Minimizing Labeling, Maximizing Performance: A Novel Approach to Nanoscale Scanning Electron Microscope (SEM) Defect Segmentation
DescriptionIn 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.
Event Type
Research Manuscript
TimeWednesday, June 262:45pm - 3:00pm PDT
Location3008, 3rd Floor
Design
Design for Manufacturability and Reliability