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, Special issue Maths and Industry, Volume 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.

Published online:
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
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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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, Special issue Maths and Industry, Volume 11 (2022) no. 1, pp. 109-114. doi : 10.5802/msia.20. https://msia.centre-mersenne.org/articles/10.5802/msia.20/

[1] Leo Breiman; Jerome Friedman; Richard A. Olshen; Charles J. Stone Classification and regression trees, Routledge, 2017 | DOI

[2] László Györfi; Michael Kohler; Adam Krzyżak; Harro Walk A distribution-free theory of nonparametric regression, 1, Springer, 2002 | DOI

[3] Trevor Hastie; Robert Tibshirani; Jerome Friedman The elements of statistical learning: data mining, inference, and prediction, Springer, 2009 | DOI

[4] Fabian Pedregosa; Gaël Varoquaux; Alexandre Gramfort; Vincent Michel; Bertrand Thirion; Olivier Grisel; Mathieu Blondel; Peter Prettenhofer; Ron Weiss; Vincent Dubourg et al. Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., Volume 12 (2011), pp. 2825-2830 | MR | Zbl

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