computer vision for wafer inspection

**Computer Vision for Wafer Inspection** is the **application of image processing and deep learning to automate the visual inspection of semiconductor wafers** — detecting defects, particles, pattern anomalies, and process signatures across optical, SEM, and other imaging modalities. **Key Computer Vision Tasks** - **Defect Detection**: Find defects that deviate from the designed pattern (die-to-die comparison, reference-based). - **Pattern Recognition**: Classify defect patterns on wafer maps (systematic vs. random signatures). - **Die-to-Database**: Compare captured images against the design layout to find missing or extra features. - **Automatic Defect Review (ADR)**: Revisit detected defects with higher resolution and classify them. **Why It Matters** - **Throughput**: CV processes wafer images at production speed (>100 wafers/hour). - **Sensitivity**: Modern algorithms detect defects smaller than the imaging resolution using statistical methods. - **Recipe Development**: ML-assisted recipe development reduces time to qualify new defect inspection recipes. **Computer Vision for Wafer Inspection** is **teaching machines to see defects** — applying image analysis at production speed to find every anomaly on every wafer.