A Methodology for Predictive Maintenance in Semiconductor Manufacturing

Authors

  • Peter Scheibelhofer ams AG, Unterpremstätten, Austria Institute of Statistics, Graz University of Technology, Austria
  • Dietmar Gleispach ams AG, Unterpremstätten, Austria
  • Günter Hayderer ams AG, Unterpremstätten, Austria
  • Ernst Stadlober Institute of Statistics, Graz University of Technology, Austria

DOI:

https://doi.org/10.17713/ajs.v41i3.171

Abstract

In order to occupy a competitive position in semiconductor industry the most important challenges a fabrication plant has to face are the reduction of manufacturing costs and the increase of production yield. Predictive maintenance is one possible way to address these challenges. In this paper we present an implementation of a universally applicable methodology based on the theory of regression trees and Random Forests to predict tool maintenance operations. We exemplarily show the application of the method by constructing a model for predictive maintenance of an ion implantation tool. To fit the
problem adequately and to allow a descriptive interpretation we introduce the remaining time until next maintenance as a response variable. By using R and adequately analyzing data acquired during wafer processing a Random Forest model is constructed. We can show that under typical production conditions
the model is able to predict a recurring maintenance operation sufficiently accurate. This example shows that better planning of maintenance operations allows for an increase in productivity and a reduction of downtime costs.

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Published

2016-02-24

How to Cite

Scheibelhofer, P., Gleispach, D., Hayderer, G., & Stadlober, E. (2016). A Methodology for Predictive Maintenance in Semiconductor Manufacturing. Austrian Journal of Statistics, 41(3), 161–173. https://doi.org/10.17713/ajs.v41i3.171

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Section

Articles