Multifeature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units
- 31 August 2016
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automation Science and Engineering
- Vol. 15 (1), 145-159
- https://doi.org/10.1109/tase.2016.2594288
Abstract
Automated inspection systems play an important role in manufacturing to guarantee higher quality and reduce production costs. 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. These defects can cause field failures and can result in customer dissatisfactions and returns. 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 of defects. This issue is aggravated by the very small sample size of available anomalies for learning, by the data imbalance problem, since the number of defective samples is significantly much smaller than the number of normal samples, and by the presence of noise. This paper presents a novel multifeature, sparse-based defect detection and classification approach that uses the stacking concept to enhance the classification accuracy. The stacking-based classifier is augmented with a novel adaptive over/downsampling technique to deal with the data imbalance problem. A new pruning technique is proposed to eliminate bad base learners. Shortage of defective units, similarities within different classes of defects, wide variation within the same defect class, and data imbalance are the basic challenges to deal with. Experimental results on real-world data from Intel show that the proposed approach results in a high classification accuracy as compared with the existing methods.Keywords
Funding Information
- Intel Corporation
This publication has 44 references indexed in Scilit:
- Roughly balanced bagging for imbalanced dataStatistical Analysis and Data Mining, 2009
- Anomaly detectionACM Computing Surveys, 2009
- Automatically countering imbalance and its empirical relationship to costData Mining and Knowledge Discovery, 2008
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- A study of the behavior of several methods for balancing machine learning training dataACM SIGKDD Explorations Newsletter, 2004
- EditorialACM SIGKDD Explorations Newsletter, 2004
- Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)The Annals of Statistics, 2000
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997
- Combining Estimates in Regression and ClassificationJournal of the American Statistical Association, 1996
- Stacked generalizationNeural Networks, 1992