Mining IC test data to optimize VLSI testing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining solves tough semiconductor manufacturing problems
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Solving regression problems with rule-based ensemble classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining for design and manufacturing
Applying Machine Learning to Semiconductor Manufacturing
IEEE Expert: Intelligent Systems and Their Applications
SNPD-SAWN '06 Proceedings of the Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Data mining for yield enhancement in semiconductor manufacturing and an empirical study
Expert Systems with Applications: An International Journal
Mining manufacturing data using genetic algorithm-based feature set decomposition
International Journal of Intelligent Systems Technologies and Applications
Improving quality control by early prediction of manufacturing outcomes
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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We describe an automated system for improving yield, power consumption and speed characteristics in the manufacture of semiconductors. Data are continually collected in the form of a history of tool usage, electrical and other real-valued measurements--a dimension of tens of thousands of features. Unique to this approach is the inference of patterns in the form of binary regression rules that demonstrate a significantly higher or lower performance value for tools relative to the overall mean for that manufacturing step. Results are filtered by knowledge-based constraints, increasing the likelihood that empirically validated rules will prove interesting and worth further investigation. This system is currently installed in the IBM 300 mm fab, manufacturing game chips and microprocessors. It has detected numerous opportunities for yield and performance improvement, saving many millions of dollars.