Defective wafer detection using neural network approach and multivariate statistical approach

Abderrahmane Boubezoul, Bouchra Ananou, Mustapha Ouladsine, Sebastien Paris, Hassan Noura

Research output: Contribution to conferencePaperpeer-review

Abstract

In this paper, we introduce an automatic classification approach based on combining two algorithms. One of them belongs to Neural Networks approach and the second is based on Multivariate Statistical approach. In this study we will present two algorithms their relative strengths and weaknesses. We realize that these two methods can complement one another resulting in better decision support system. Integrating these complementary features is one way to develop hybrid system that could overcome the limitations of individual solution strategies. We evaluate this hybrid system on data provided by semiconductor fab, especially on Parametric Tests (PT) data. This procedure was successfully validated at PT data provided by STMicroelectronics - Rousset fab.

Original languageEnglish
Pages125-130
Number of pages6
Publication statusPublished - 2007
Event3rd International Conference on Advances in Vehicle Control and Safety 2007, AVCS 2007 and 3rd International Conference on Integrated Modeling and Analysis in Applied Control and Automation, IMAACA 2007, Held at the IMSM 2007 - Buenos Aires, Argentina
Duration: Feb 8 2007Feb 10 2007

Other

Other3rd International Conference on Advances in Vehicle Control and Safety 2007, AVCS 2007 and 3rd International Conference on Integrated Modeling and Analysis in Applied Control and Automation, IMAACA 2007, Held at the IMSM 2007
Country/TerritoryArgentina
CityBuenos Aires
Period2/8/072/10/07

Keywords

  • GRLVQ
  • Parametric tests

ASJC Scopus subject areas

  • Automotive Engineering
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

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