A Methodology for Predictive Maintenance in Semiconductor Manufacturing
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 pr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Austrian Statistical Society
2016-02-01
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| Series: | Austrian Journal of Statistics |
| Online Access: | http://www.ajs.or.at/index.php/ajs/article/view/171 |
| _version_ | 1798029473998700544 |
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| author | Peter Scheibelhofer Dietmar Gleispach Günter Hayderer Ernst Stadlober |
| author_facet | Peter Scheibelhofer Dietmar Gleispach Günter Hayderer Ernst Stadlober |
| author_sort | Peter Scheibelhofer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T19:24:42Z |
| format | Article |
| id | doaj.art-d46e1dbc0b8d41a5ba7595ea9e22c3e0 |
| institution | Directory Open Access Journal |
| issn | 1026-597X |
| language | English |
| last_indexed | 2024-04-11T19:24:42Z |
| publishDate | 2016-02-01 |
| publisher | Austrian Statistical Society |
| record_format | Article |
| series | Austrian Journal of Statistics |
| spelling | doaj.art-d46e1dbc0b8d41a5ba7595ea9e22c3e02022-12-22T04:07:11ZengAustrian Statistical SocietyAustrian Journal of Statistics1026-597X2016-02-0141310.17713/ajs.v41i3.171A Methodology for Predictive Maintenance in Semiconductor ManufacturingPeter Scheibelhofer0Dietmar Gleispach1Günter Hayderer2Ernst Stadlober3ams AG, Unterpremstätten, Austria Institute of Statistics, Graz University of Technology, Austriaams AG, Unterpremstätten, Austriaams AG, Unterpremstätten, AustriaInstitute of Statistics, Graz University of Technology, AustriaIn 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.http://www.ajs.or.at/index.php/ajs/article/view/171 |
| spellingShingle | Peter Scheibelhofer Dietmar Gleispach Günter Hayderer Ernst Stadlober A Methodology for Predictive Maintenance in Semiconductor Manufacturing Austrian Journal of Statistics |
| title | A Methodology for Predictive Maintenance in Semiconductor Manufacturing |
| title_full | A Methodology for Predictive Maintenance in Semiconductor Manufacturing |
| title_fullStr | A Methodology for Predictive Maintenance in Semiconductor Manufacturing |
| title_full_unstemmed | A Methodology for Predictive Maintenance in Semiconductor Manufacturing |
| title_short | A Methodology for Predictive Maintenance in Semiconductor Manufacturing |
| title_sort | methodology for predictive maintenance in semiconductor manufacturing |
| url | http://www.ajs.or.at/index.php/ajs/article/view/171 |
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