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.
@article{MSIA_2022__11_1_109_0,
author = {Boulanger, Jean-Fran\c{c}ois and Corset, Franck and Iutzeler, Franck and Lelong, J\'er\^ome},
title = {Classifying and explaining defects with small data for the semiconductor industry},
journal = {MathematicS In Action},
pages = {109--114},
publisher = {Soci\'et\'e de Math\'ematiques Appliqu\'ees et Industrielles},
volume = {11},
number = {1},
year = {2022},
doi = {10.5802/msia.20},
language = {en},
url = {http://www.numdam.org/articles/10.5802/msia.20/}
}
TY - JOUR AU - Boulanger, Jean-François AU - Corset, Franck AU - Iutzeler, Franck AU - Lelong, Jérôme TI - Classifying and explaining defects with small data for the semiconductor industry JO - MathematicS In Action PY - 2022 SP - 109 EP - 114 VL - 11 IS - 1 PB - Société de Mathématiques Appliquées et Industrielles UR - http://www.numdam.org/articles/10.5802/msia.20/ DO - 10.5802/msia.20 LA - en ID - MSIA_2022__11_1_109_0 ER -
%0 Journal Article %A Boulanger, Jean-François %A Corset, Franck %A Iutzeler, Franck %A Lelong, Jérôme %T Classifying and explaining defects with small data for the semiconductor industry %J MathematicS In Action %D 2022 %P 109-114 %V 11 %N 1 %I Société de Mathématiques Appliquées et Industrielles %U http://www.numdam.org/articles/10.5802/msia.20/ %R 10.5802/msia.20 %G en %F MSIA_2022__11_1_109_0
Boulanger, Jean-François; Corset, Franck; Iutzeler, Franck; Lelong, Jérôme. 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. http://www.numdam.org/articles/10.5802/msia.20/
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