학술논문
Exploiting 2D Coordinates as Bayesian Priors for Deep Learning Defect Classification of SEM Images
Document Type
Periodical
Author
Source
IEEE Transactions on Semiconductor Manufacturing IEEE Trans. Semicond. Manufact. Semiconductor Manufacturing, IEEE Transactions on. 34(3):436-439 Aug, 2021
Subject
Language
ISSN
0894-6507
1558-2345
1558-2345
Abstract
Deep Learning approaches have revolutionized in the past decade the field of Computer Vision and, as a consequence, they are having a major impact in Industry 4.0 applications like automatic defect classification. Nevertheless, additional data, beside the image/video itself, is typically never exploited in a defect classification module: this aspect, given the abundance of data in data-intensive manufacturing environments (like semiconductor manufacturing) represents a missed opportunity. In this work we present a use case related to Scanning Electron Microscope (SEM) images where we exploit a Bayesian approach to improve defect classification. We validate our approach on a real-world case study and by employing modern Deep Learning architectures for classification.