№ files_lp_3_process_9_53710
Research article presenting a three-stage Render-and-Compare deep learning architecture for Die-to-Database wafer inspection that addresses CAD–SEM domain mismatch and evaluates defect detection performance on a large synthetic dataset.
Type of document: Original Research Article
Field: Semiconductor manufacturing and wafer inspection
Primary methodology: Hybrid Die-to-Database (D2DB) deep learning framework
Comparative methods discussed: Die-to-Die (D2D) inspection and Die-to-Database (D2DB) inspection
Key technologies: Physics-informed rendering, Pix2Pix conditional GAN, Siamese neural network
Dataset size: 20,000-pattern synthetic dataset
Reported performance: False alarm rate below 4%; topological defect recall 93%
Defect focus: Systematic and topological defects including bridges and opens
Application domain: Advanced semiconductor nodes (5 nm, 3 nm, GAA technologies)
Keywords: D2DB inspection, D2D inspection, semiconductor defect detection, CAD-to-SEM rendering, topology-aware defect classification
Price: 8 / 10 USD
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