Prediction of integral type failures in semiconductor manufacturing through classification methods

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

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

6 Citations (Scopus)

Abstract

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset.

Original languageEnglish
Title of host publicationIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
DOIs
Publication statusPublished - 24 Dec 2013
Event2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013 - Cagliari, Italy
Duration: 10 Sep 201313 Sep 2013

Conference

Conference2013 IEEE 18th International Conference on Emerging Technologies and Factory Automation, ETFA 2013
CountryItaly
CityCagliari
Period10/09/201313/09/2013

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Computer Science Applications

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  • Cite this

    Susto, G. A., McLoone, S., Pagano, D., Schirru, A., Pampuri, S., & Beghi, A. (2013). Prediction of integral type failures in semiconductor manufacturing through classification methods. In IEEE International Conference on Emerging Technologies and Factory Automation, ETFA [6648127] https://doi.org/10.1109/ETFA.2013.6648127