The Strength of Weak Learnability
Machine Learning
Mining IC test data to optimize VLSI testing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Applying Machine Learning to Semiconductor Manufacturing
IEEE Expert: Intelligent Systems and Their Applications
Learning dynamic temporal graphs for oil-production equipment monitoring system
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Stacking recommendation engines with additional meta-features
Proceedings of the third ACM conference on Recommender systems
The million dollar programming prize
IEEE Spectrum
Rule-based data mining for yield improvement in semiconductor manufacturing
Applied Intelligence
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We describe methods for continual prediction of manufactured product quality prior to final testing. In our most expansive modeling approach, an estimated final characteristic of a product is updated after each manufacturing operation. Our initial application is for the manufacture of microprocessors, and we predict final microprocessor speed. Using these predictions, early corrective manufacturing actions may be taken to increase the speed of expected slow wafers (a collection of microprocessors) or reduce the speed of fast wafers. Such predictions may also be used to initiate corrective supply chain management actions. Developing statistical learning models for this task has many complicating factors: (a) a temporally unstable population (b) missing data that is a result of sparsely sampled measurements and (c) relatively few available measurements prior to corrective action opportunities. In a real manufacturing pilot application, our automated models selected 125 fast wafers in real-time. As predicted, those wafers were significantly faster than average. During manufacture, downstream corrective processing restored 25 nominally unacceptable wafers to normal operation.