Méthode d'analyse de données pour le diagnostic a posteriori de défauts de production - Application au secteur de la microélectronique

Abstract : Controlling the performance of a manufacturing site and the rapid identification of quality loss causes remain a daily challenge for manufacturers, who face continuing competition. In this context, this thesis aims to provide an analytical approach for the rapid identification of defect origins, by exploring data available thanks to different quality control systems, such FDC, metrology, parametric tests PT and the Electrical Wafer Sorting EWS. The proposed method, named CLARIF, combines three complementary data mining techniques namely clustering, association rules and decision trees induction. This method is based on unsupervised generation of a set of potentially problematic production modes, which are characterized by specific manufacturing conditions. Thus, we provide an analysis which descends to the level of equipment operating parameters. The originality of this method consists on (1) a pre-treatment step to identify spatial patterns from quality control data, (2) an unsupervised generation of manufacturing modes candidates to explain the quality loss case. We optimize the generation of association rules through the proposed ARCI algorithm, which is an adaptation of the famous association rules mining algorithm, APRIORI to integrate the constraints specific to our issue and filtering quality indicators, namely confidence, contribution and complexity, in order to identify the most interesting rules. Finally, we defined a Knowledge Discovery from Databases process, enabling to guide the user in applying CLARIF to explain both local and global quality loss problems.
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Hasna Yahyaoui. Méthode d'analyse de données pour le diagnostic a posteriori de défauts de production - Application au secteur de la microélectronique. Autre. Ecole Nationale Supérieure des Mines de Saint-Etienne, 2015. Français. ⟨NNT : 2015EMSE0795⟩. ⟨tel-01282255⟩

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