@inproceedings{7298abf654b84d7b8f1f585d81bb9828,
title = "Multi-feature sparse-based defect detection and classification in semiconductor units",
abstract = "Automated inspection systems have been used extensively for high-speed defect detection, gaging and quality control. In the semiconductor manufacturing industry, assembly and testing processes are getting more complex resulting in a greater tendency of defects to impact the production process. Currently available defect detection and classification systems are customized and hard-wired to the detection of particular classes of defects and cannot deal with new unknown classes. Shortage of defective units, similarities within different classes of defects, wide variations within the same defect class, and data imbalance are the basic challenges for this problem. This paper presents a novel stacking-based multi-feature, sparse-based defect detection and classification method that is robust to data imbalance and low number of training samples. Experimental results on real-world data from Intel show that the proposed approach results in a high classification accuracy as compared to existing methods.",
keywords = "Automation, Data mining, Feature extraction, Image classification, Sparse coding, Stacking",
author = "Bashar Haddad and Lina Karam and Jieping Ye and Nital Patel and Martin Braun",
year = "2016",
month = aug,
day = "3",
doi = "10.1109/ICIP.2016.7532458",
language = "English (US)",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "754--758",
booktitle = "2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings",
note = "23rd IEEE International Conference on Image Processing, ICIP 2016 ; Conference date: 25-09-2016 Through 28-09-2016",
}