Unsupervised Defect Detection Based on Self-Supervised Transformers

Effective management of wafer defects is crucial for improving the yield of integrated circuit (IC) chip manufacturing. Compared to traditional defect analysis performed by human experts, deep learning-based automatic defect detection can significantly enhance both the speed and accuracy of the process. This helps engineers identify root causes more quickly, thereby improving yield. However, the training of these detection models requires substantial manual annotation, which can be costly and limits the widespread application of automatic defect detection. To address this challenge, we propose an unsupervised defect localization method that requires no training data. This method achieved a defect detection accuracy (ACC) of 92.5% (with IoU threshold = 0.3) on both scanning electron microscopy (SEM) and optical microscope (OM) images. By significantly reducing the need for manual annotation, this new approach paves the way for fully automated large-scale data training in the future.