文章基本信息

标题：Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy
本地全文：下载
作者：Luca Puggini ; Seán McLoone
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：5
页码：132-137
DOI：10.1016/j.ifacol.2016.07.102
语种：English
出版社：Elsevier
摘要：AbstractTo maintain the pace of development set by Moore’s law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.
关键词：KeywordsSemiconductorsFault DetectionDimensionality ReductionOC-SVMOES Spectrum