Image Segmentation for Defects is the pixel-level classification of wafer and device images into defect and non-defect regions — providing precise defect outlines, sizes, and areas rather than just bounding boxes, enabling accurate dimensional measurement of defects.

Deep Learning Architectures

• U-Net: Encoder-decoder architecture with skip connections — the standard for defect segmentation.
• Mask R-CNN: Instance segmentation that separates individual defects even when overlapping.
• DeepLab: Atrous convolutions for multi-scale segmentation of complex defect patterns.
• Semantic vs. Instance: Semantic segments by class (defect type). Instance separates individual defects.

Why It Matters

• Precise Sizing: Segmentation provides exact defect area, perimeter, and shape — critical for severity assessment.
• Kill Analysis: Precise defect outlines enable accurate overlap analysis with circuit patterns for kill probability.
• SEM Review: Automated segmentation of SEM review images replaces manual outlining.

Image Segmentation is pixel-perfect defect delineation — tracing the exact boundary of every defect for precise dimensional and kill-probability analysis.