no. 1
Classifying and explaining defects with small data for the semiconductor industry
Jean-François Boulanger1 ; Franck Corset2 ; Franck Iutzeler2 ; Jérôme Lelong2
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, Tome 11 (2022) no. 1, pp. 109-114.

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
Jean-François Boulanger 1 ; Franck Corset 2 ; Franck Iutzeler 2 ; Jérôme Lelong 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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     title = {Classifying and explaining defects with small data for the semiconductor industry},
     journal = {MathematicS In Action},
     pages = {109--114},
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Jean-François Boulanger; Franck Corset; Franck Iutzeler; Jérôme Lelong. Classifying and explaining defects with small data for the semiconductor industry. MathematicS In Action, Tome 11 (2022) no. 1, pp. 109-114. doi : 10.5802/msia.20. https://msia.centre-mersenne.org/articles/10.5802/msia.20/

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