  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 from different models in
order to achieve better performance on classification and detection of defects.
As CDSEM images inherently contain a significant level of noise, detailed
feature information is often shadowed by noise. For certain resist profiles,
the challenge is also to differentiate between a microbridge, footing, break,
and zones of probable breaks. Therefore, we have applied an unsupervised
machine learning model to denoise the SEM images to remove the False-Positive
defects and optimize the effect of stochastic noise on structured pixels for
better metrology and enhanced defect inspection. We repeated the defect
inspection step with the same trained model and performed a comparative
analysis for "robustness" and "accuracy" metric with conventional approach for
both noisy/denoised image pair. The proposed ensemble method demonstrates
improvement of the average precision metric (mAP) of the most difficult defect
classes. In this work we have developed a novel robust supervised deep learning
training scheme to accurately classify as well as localize different defect
types in SEM images with high degree of accuracy. Our proposed approach
demonstrates its effectiveness both quantitatively and qualitatively.
