The final properties of sophisticated products can be affected by many unapparent dependencies within the manufacturing process, and the products integrity can often only be checked in a final measurement. Troubleshooting can therefore be very tedious if not impossible in large assembly lines. In this paper we show that Feature Selection is an efficient tool for serial-grouped lines to reveal causes for irregularities in product attributes. We compare the performance of several methods for Feature Selection on real-world problems in mass-production of semiconductor devices. Note to Practitioners We present a data based procedure to localize flaws in large production lines: using the results of final quality inspections and information about which machines processed which batches, we are able to identify machines which cause low yield.
Author(s): | Pfingsten, T. and Herrmann, DJL. and Schnitzler, T. and Feustel, A. and Schölkopf, B. |
Journal: | IEEE Transactions on Automation Science and Engineering |
Volume: | 4 |
Number (issue): | 3 |
Pages: | 465-469 |
Year: | 2007 |
Month: | July |
Day: | 0 |
Bibtex Type: | Article (article) |
DOI: | 10.1109/TASE.2006.888054 |
Digital: | 0 |
Electronic Archiving: | grant_archive |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
BibTex
@article{3611, title = {Feature Selection for Trouble Shooting in Complex Assembly Lines}, journal = {IEEE Transactions on Automation Science and Engineering}, abstract = {The final properties of sophisticated products can be affected by many unapparent dependencies within the manufacturing process, and the products integrity can often only be checked in a final measurement. Troubleshooting can therefore be very tedious if not impossible in large assembly lines. In this paper we show that Feature Selection is an efficient tool for serial-grouped lines to reveal causes for irregularities in product attributes. We compare the performance of several methods for Feature Selection on real-world problems in mass-production of semiconductor devices. Note to Practitioners We present a data based procedure to localize flaws in large production lines: using the results of final quality inspections and information about which machines processed which batches, we are able to identify machines which cause low yield.}, volume = {4}, number = {3}, pages = {465-469}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = jul, year = {2007}, slug = {3611}, author = {Pfingsten, T. and Herrmann, DJL. and Schnitzler, T. and Feustel, A. and Sch{\"o}lkopf, B.}, month_numeric = {7} }