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
N1 - Publisher Copyright:
© 2016 IEEE.
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
AN - SCOPUS:85006825746
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 754
EP - 758
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Image Processing, ICIP 2016
Y2 - 25 September 2016 through 28 September 2016
ER -