Semiconductor Defect Inspection

Semiconductor defect inspection aims to automatically identify and classify nanoscale defects in semiconductor wafers, crucial for ensuring high manufacturing yields and product reliability. Current research heavily utilizes deep learning, employing architectures like YOLO, Mask R-CNN, and CenterNet, often enhanced with techniques to address data scarcity (e.g., generative models) and class imbalance. These advancements improve defect detection accuracy and efficiency, impacting both semiconductor manufacturing processes and the development of advanced imaging and analysis tools.