Deep learning-based image quality adaptation for die-to-database defect inspection

While extreme ultraviolet lithography has contributed to sub-10nm microfabrication, there are concerns about stochastic defects. Thus, the process evaluation requires fast and precise inspection of entire wafers. To do this, large field-of-view (FoV) e-beam inspection has been introduced. However, large FoV inspection sometimes suffers from image degradations due to aberrations and/or charged wafers that cause false detections during image comparison inspection. To reduce these false detections, we developed a deep learning-based image adaptation method to reduce the difference between the reference image and degraded inspection image. Here, the adapter that simply minimizes the difference often falls into over-adaptation that eliminates the difference in defect characteristics and decreases detection sensitivity. To address this, we introduced a patch-wise blind-spot network (PwBSN) that recognizes only the image degradation by leveraging the property that the defect region is smaller than the image degradation region. Since the PwBSN can only use surrounding regions due to its architectural constraints, it only minimizes the difference in degradations except for defects smaller than patches. We applied this method to deep learning-based die-to-database defect inspection. The evaluation on SEM images showed that the proposed method detects only defects, while a conventional method detects both defects and image degradation regions.