Continuous prediction of manufacturing performance throughout the production lifecycle

Sholom M. Weiss (), Amit Dhurandhar (), Robert J. Baseman (), Brian F. White (), Ronald Logan (), Jonathan K. Winslow () and Daniel Poindexter ()
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Sholom M. Weiss: IBM Research
Amit Dhurandhar: IBM Research
Robert J. Baseman: IBM Research
Brian F. White: IBM Research
Ronald Logan: IBM Microelectronics
Jonathan K. Winslow: IBM Microelectronics
Daniel Poindexter: IBM Microelectronics

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 4, No 4, 763 pages

Abstract: Abstract 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.

Keywords: Manufacturing; Data mining; Prediction (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-014-0911-x

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