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.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 754-758 |
| Number of pages | 5 |
| Volume | 2016-August |
| ISBN (Electronic) | 9781467399616 |
| DOIs | |
| State | Published - Aug 3 2016 |
| Event | 23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States Duration: Sep 25 2016 → Sep 28 2016 |
Other
| Other | 23rd IEEE International Conference on Image Processing, ICIP 2016 |
|---|---|
| Country | United States |
| City | Phoenix |
| Period | 9/25/16 → 9/28/16 |
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Keywords
- Automation
- Data mining
- Feature extraction
- Image classification
- Sparse coding
- Stacking
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Multi-feature sparse-based defect detection and classification in semiconductor units. / Haddad, Bashar; Karam, Lina; Ye, Jieping; Patel, Nital; Braun, Martin.
2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August IEEE Computer Society, 2016. p. 754-758 7532458.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multi-feature sparse-based defect detection and classification in semiconductor units
AU - Haddad, Bashar
AU - Karam, Lina
AU - Ye, Jieping
AU - Patel, Nital
AU - Braun, Martin
PY - 2016/8/3
Y1 - 2016/8/3
N2 - 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.
AB - 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.
KW - Automation
KW - Data mining
KW - Feature extraction
KW - Image classification
KW - Sparse coding
KW - Stacking
UR - http://www.scopus.com/inward/record.url?scp=85006825746&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006825746&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2016.7532458
DO - 10.1109/ICIP.2016.7532458
M3 - Conference contribution
VL - 2016-August
SP - 754
EP - 758
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
ER -