LASSO-based Health Indicator Extraction Method for Semiconductor Manufacturing Processes

被引：0

作者：

Jamal, Dima E. L. ^{[1
]}

Ananou, Bouchra ^{[1
]}

Graton, Guillaume ^{[2
]}

Ouladsine, Moustapha ^{[1
]}

Pinaton, Jacques ^{[3
]}

机构：

[1] Aix Marseille Univ, Univ Toulon, CNRS, LIS UMR 7020, Ave Escadrille Normandie Niemen, F-13397 Marseille 20, France

[2] Ecole Cent Marseille, Technopole Chateau Gombert, 38 Rue Frederic Joliot Curie, F-13451 Marseille, France

[3] STMicroelect Rousset, 190 Ave Celestin Coq, F-13106 Rousset, France

来源：

2022 EUROPEAN CONTROL CONFERENCE (ECC) | 2022年

关键词：

Health indicator extraction; feature selection; LASSO; semiconductor manufacturing; BATCH PROCESSES; SELECTION; STEPS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Over the last few years, with the increasing worldwide competition, semiconductor industries have had to constantly innovate in order to enhance their performance, productivity and minimize the downtime. Monitoring the state of health of their equipment units is important to avoid machine failures and to plan maintenance actions. For that, a novel approach for health indicator extraction named Significant Points combined to the Least Absolute Shrinkage and Selection Operator (SP-LASSO) is proposed in this paper. It deals with the problem of high dimensional data and the specificity of the health indicator in real industrial cases. The proposed method performs feature selection and health indicator extraction and it is mainly based on LASSO. A numerical application on simulated data illustrates the accuracy of this approach.

引用

页码：491 / 496

页数：6

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