C4.5: programs for machine learning
C4.5: programs for machine learning
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Communications of the ACM
Automated detection of hereditary syndromes using data mining
Computers and Biomedical Research
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining and Knowledge Discovery in Databases: Implications for Scientific Databases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Recognition of semiconductor defect patterns using spatial filtering and spectral clustering
Expert Systems with Applications: An International Journal
Modeling semiconductor testing job scheduling and dynamic testing machine configuration
Expert Systems with Applications: An International Journal
Hybrid machine learning to improve predictive performance
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Predictive Performance of Clustered Feature-Weighting Case-Based Reasoning
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Separation of composite defect patterns on wafer bin map using support vector clustering
Expert Systems with Applications: An International Journal
Recognizing yield patterns through hybrid applications of machine learning techniques
Information Sciences: an International Journal
Proceedings of the 40th Conference on Winter Simulation
A Bayesian ranking scheme for supporting cost-effectiveyield diagnosis services
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Implementing a data mining solution for enhancing carpet manufacturing productivity
Knowledge-Based Systems
Rule-based data mining for yield improvement in semiconductor manufacturing
Applied Intelligence
Review: A review of data mining applications for quality improvement in manufacturing industry
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Journal of Intelligent Manufacturing
Intelligence modeling for coping strategies to reduce emergency department overcrowding in hospitals
Journal of Intelligent Manufacturing
Identifying ill tool combinations via Gibbs sampler for semiconductor manufacturing yield diagnosis
Proceedings of the Winter Simulation Conference
Engineering Applications of Artificial Intelligence
Detection and classification of defect patterns in optical inspection using support vector machines
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Hi-index | 12.07 |
During wafer fabrication, process data, equipment data, and lot history will be automatically or semi-automatically recorded and accumulated in database for monitoring the process, diagnosing faults, and managing manufacturing. However, in high-tech industry such as semiconductor manufacturing, many factors that are interrelated affect the yield of fabricated wafers. Engineers who rely on personal domain knowledge cannot find possible root causes of defects rapidly and effectively. This study aims to develop a framework for data mining and knowledge discovery from database that consists of a Kruskal-Wallis test, K-means clustering, and the variance reduction splitting criterion to investigate the huge amount of semiconductor manufacturing data and infer possible causes of faults and manufacturing process variations. The extracted information and knowledge is helpful to engineers as a basis for trouble shooting and defect diagnosis. We validated this approach with an empirical study in a semiconductor foundry company in Taiwan and the results demonstrated the practical viability of this approach.