  This work is addressing the problem of defect anomaly detection based on a
clean reference image. Specifically, we focus on SEM semiconductor defects in
addition to several natural image anomalies. There are well-known methods to
create a simulation of an artificial reference image by its defect specimen. In
this work, we introduce several applications for this capability, that the
simulated reference is beneficial for improving their results. Among these
defect detection methods are classic computer vision applied on
difference-image, supervised deep-learning (DL) based on human labels, and
unsupervised DL which is trained on feature-level patterns of normal reference
images. We show in this study how to incorporate correctly the simulated
reference image for these defect and anomaly detection applications. As our
experiment demonstrates, simulated reference achieves higher performance than
the real reference of an image of a defect and anomaly. This advantage of
simulated reference occurs mainly due to the less noise and geometric
variations together with better alignment and registration to the original
defect background.
