Abstract
Automated inspection has become a vital part of quality control during semiconductor wafer production. Current processes are focused on finding defects via variation from a ‘golden’ image using pixel to pixel comparisons or utilization of opaque neural network-based approaches. In this paper we present an approach which uses deep learning methods to classify defects on semiconductor die images and show the experimental steps taken in order to produce a highly accurate system based on previous models.
Original language | English |
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Title of host publication | 2022 IEEE 20th International Conference on Industrial Informatics (INDIN) |
Publisher | IEEE |
Pages | 572-577 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-7568-3 |
ISBN (Print) | 978-1-7281-7569-0 |
DOIs | |
Publication status | Published online - 15 Dec 2022 |
Event | IEEE International Conference on Industrial Informatics, - Perth, Australia Duration: 25 Jul 2022 → 28 Jul 2022 https://2022.ieee-indin.org/ |
Conference
Conference | IEEE International Conference on Industrial Informatics, |
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Abbreviated title | INDIN22 |
Country/Territory | Australia |
City | Perth |
Period | 25/07/22 → 28/07/22 |
Internet address |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Defect Detection
- Defect Classification
- Semiconductor wafers
- Image Processing
- Deep learning
- Transfer Learning
- Convolutional Neural Network
- Convolutional Neural Networks