Deep Learning-Based Defect Classification and Detection in SEM Images. (arXiv:2206.13505v1 [eess.IV])

This proposes a novel ensemble deep learning-based model to accurately
classify, detect and localize different defect categories for aggressive
pitches and thin resists (High NA applications).In particular, we train
RetinaNet models using different ResNet, VGGNet architectures as backbone and
present the comparison between the accuracies of these models and their
performance analysis on SEM images with different types of defect patterns such
as bridge, break and line collapses. Finally, we propose a preference-based
ensemble strategy to combine the output predictions …

arxiv classification deep learning detection images learning sem