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dc.contributor.authorKosla, C.en
dc.contributor.authorHill, M.en
dc.contributor.editor-en
dc.contributor.other-en
dc.date.accessioned2017-03-21T13:26:15Z
dc.date.available2017-03-21T13:26:15Z
dc.date.copyright2013en
dc.date.issued2013-06
dc.identifier.citationKosla, C. and Hill, M. (2013) 'Extraction of reliability indicators from large-scale, highly variable timing data of MEMS switches', presented at 24th IET Irish Signals and Systems Conference ( ISSC 2013), 20 - 21 Jun.en
dc.identifier.isbn978-1-84919-754-0en
dc.identifier.issn-en
dc.identifier.urihttps://research.thea.ie/handle/20.500.12065/1037
dc.description.abstractIn this paper we present a method for monitoring and processing large-scale highly variable and nonlinear reliability data for MEMS RF Switches. The data is generated by measuring the switch actuation dynamics and a combination of statistical methods are applied to extract the switch closure/opening time. The signal processing is performed in a 2-step approach encompassing basic parametric and nonparametric statistical methods to correctly categorize the obtained datasets, supplemented with a mean-first derivative algorithm to extract the reliability indicators from the filtered data. The presented procedure proves to be highly accurate generating very low error in dataset classification. The acquired results give an insight into the evolution of switch health throughout its whole operation cycle and can lead to further understanding of failure mechanisms in micromechanical structures.en
dc.formatPdfen
dc.language.isoenen
dc.publisherSignals and Systems Conference (ISSC 2013), 24th IET Irishen
dc.subjectMEMS reliabilityen
dc.subjectTo/Tcen
dc.subjectData analysisen
dc.subjectSignal processingen
dc.subjectStatisticsen
dc.subjectSwitch dynamicsen
dc.titleExtraction of reliability indicators from large-scale, highly variable timing data of MEMS switchesen
dc.typeConference Presentationen
dc.description.peerreviewYesen
dc.identifier.urlhttp://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?arnumber=6621229&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6620810%29en
dc.subject.departmentScienceen


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