<ul><li>Defect detection in photolithographic patterns is crucial for semiconductor manufacturing during EUV pattering.</li><li>The small size of defects in patterns leads to false or missed detections during inspection.</li><li>A study focuses on using deep learning models trained on synthetic data for defect detection, where SEM images with known defects are artificially generated and annotated.</li><li>The YOLOv8 object detection model shows the best mean average precision of 96% for detecting smaller defects, outperforming EfficientNet and SSD.</li></ul>

Defect Detection in Photolithographic Patterns Using Deep Learning Models Trained on Synthetic Data

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