Geodesic
Classifying and explaining defects with small data for the semiconductor industry
Boulanger, Jean-François1 ; Corset, Franck2 ; Iutzeler, Franck2 ; Lelong, Jérôme2
1 Unity SC , 611 rue Aristide Bergès, Z.A. de Pré Millet, 38330, Montbonnot-Saint-Martin, France
2 Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
MathematicS In Action, Special issue Maths and Industry, Tome 11 (2022) no. 1, pp. 109-114.

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In this work, we present an automatic classifier of wafer defects for the semiconductor industry. Hopefully defects are rare, but this puts the classifying problem in a small data context. We propose a fast and fully reproducible approach based on decision trees. The main interest of using decision trees lies in obtaining a highly explicable classifier, which makes the origin of the defect easy to identify.

Publié le :
DOI : 10.5802/msia.20

Boulanger, Jean-François 1 ; Corset, Franck 2 ; Iutzeler, Franck 2 ; Lelong, Jérôme 2

1 Unity SC , 611 rue Aristide Bergès, Z.A. de Pré Millet, 38330, Montbonnot-Saint-Martin, France
2 Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
Licence : CC-BY 4.0
Droits d'auteur : Les auteurs conservent leurs droits 
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Boulanger, Jean-François; Corset, Franck; Iutzeler, Franck; Lelong, Jérôme. Classifying and explaining defects with small data for the semiconductor industry. MathematicS In Action, Special issue Maths and Industry, Tome 11 (2022) no. 1, pp. 109-114. doi : 10.5802/msia.20. http://geodesic.mathdoc.fr/articles/10.5802/msia.20/

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