Due to potential pitch reduction, the semiconductor industry is adopting
High-NA EUVL technology. However, its low depth of focus presents challenges
for High Volume Manufacturing. To address this, suppliers are exploring thinner
photoresists and new underlayers/hardmasks. These may suffer from poor SNR,
complicating defect detection. Vision-based ML algorithms offer a promising
solution for semiconductor defect inspection. However, developing a robust ML
model across various image resolutions without explicit training remains a
challenge for nano-scale defect inspection. This research's goal is to propose
a scale-invariant ADCD framework capable to upscale images, addressing this
issue. We propose an improvised ADCD framework as SEMI-SuperYOLO-NAS, which
builds upon the baseline YOLO-NAS architecture. This framework integrates a SR
assisted branch to aid in learning HR features by the defect detection
backbone, particularly for detecting nano-scale defect instances from LR
images. Additionally, the SR-assisted branch can recursively generate upscaled
images from their corresponding downscaled counterparts, enabling defect
detection inference across various image resolutions without requiring explicit
training. Moreover, we investigate improved data augmentation strategy aimed at
generating diverse and realistic training datasets to enhance model
performance. We have evaluated our proposed approach using two original FAB
datasets obtained from two distinct processes and captured using two different
imaging tools. Finally, we demonstrate zero-shot inference for our model on a
new, originating from a process condition distinct from the training dataset
and possessing different Pitch characteristics. Experimental validation
demonstrates that our proposed ADCD framework aids in increasing the throughput
of imaging tools for defect inspection by reducing the required image pixel
resolutions.

该研究提出了一个可提高成像工具缺陷检测吞吐量的 ADCD 框架，该框架通过在低分辨率图像中检测纳米级缺陷实例来解决高深度聚焦挑战，并通过 SR 辅助分支实现不同分辨率图像上的缺陷检测推断，无需明确训练。