A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation

Gian Antonio Susto*, Andrea Schirru, Simone Pampuri, Daniele Pagano, Sean McLoone, Alessandro Beghi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset.

Original languageEnglish
Title of host publicationIEEE International Conference on Automation Science and Engineering
Pages195-200
Number of pages6
DOIs
Publication statusPublished - 01 Dec 2013
Event2013 IEEE International Conference on Automation Science and Engineering, CASE 2013 - WI, Madison, United States
Duration: 17 Aug 201320 Aug 2013

Conference

Conference2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
CountryUnited States
CityMadison
Period17/08/201320/08/2013

Keywords

  • Classification Methods
  • Ion-Implantation
  • Predictive Maintenance
  • Semiconductor Manufacturing
  • Support Vector Machines

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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    Susto, G. A., Schirru, A., Pampuri, S., Pagano, D., McLoone, S., & Beghi, A. (2013). A predictive maintenance system for integral type faults based on support vector machines: An application to ion implantation. In IEEE International Conference on Automation Science and Engineering (pp. 195-200). [6653952] https://doi.org/10.1109/CoASE.2013.6653952