Defect Detection Approaches Based on Simulated Reference Image

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 spe...

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Bibliographic Details
Published inarXiv.org
Main Authors Ofir, Nati, Yotam Ben Shoshan, Badanes, Ran, Sherman, Boris
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 21.03.2023
Subjects
Anomalies
Computer vision
Defects
Simulation
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Summary: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.
ISSN:2331-8422