Exploiting 2D Coordinates as Bayesian Priors for Deep Learning Defect Classification of SEM Images
| Autor: | Yao Yang, Alessandro Beghi, Gian Antonio Susto, Mattia Carletti, Natalie Gentner, Marco Maggipinto, Simone Arena, Andreas Kyek, Yury Bodrov |
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| Rok vydání: | 2021 |
| Předmět: |
Exploit
Computer science business.industry Semiconductor device fabrication Deep learning Bayesian probability convolutional neural network deep learning Pattern recognition Bayesian priors Condensed Matter Physics computer vision Industrial and Manufacturing Engineering Field (computer science) Electronic Optical and Magnetic Materials Image (mathematics) defect classification Artificial intelligence Electrical and Electronic Engineering business Missed opportunity |
| Zdroj: | IEEE Transactions on Semiconductor Manufacturing. 34:436-439 |
| ISSN: | 1558-2345 0894-6507 |
| DOI: | 10.1109/tsm.2021.3088798 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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