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dc.contributor.authorGámiz, María Luz
dc.contributor.authorKalén, Anton
dc.contributor.authorNozal Cañadas, Rafael
dc.contributor.authorRaya-Miranda, Rocío
dc.date.accessioned2022-08-04T11:45:59Z
dc.date.available2022-08-04T11:45:59Z
dc.date.issued2022-04-04
dc.description.abstractOur practical motivation is the analysis of potential correlations between spectral noise current and threshold voltage from common on-wafer MOSFETs. The usual strategy leads to the use of standard techniques based on Normal linear regression easily accessible in all statistical software (both free or commercial). However, these statistical methods are not appropriate because the assumptions they lie on are not met. More sophisticated methods are required. A new strategy based on the most novel nonparametric techniques which are data-driven and thus free from questionable parametric assumptions is proposed. A backfitting algorithm accounting for random effects and nonparametric regression is designed and implemented. The nature of the correlation between threshold voltage and noise is examined by conducting a statistical test, which is based on a novel technique that summarizes in a color map all the relevant information of the data. The way the results are presented in the plot makes it easy for a non-expert in data analysis to understand what is underlying. The good performance of the method is proven through simulations and it is applied to a data case in a field where these modern statistical techniques are novel and result very efficient.en_US
dc.identifier.citationGámiz, Kalén, Nozal Cañadas, Raya-Miranda. Statistical supervised learning with engineering data: a case study of low frequency noise measured on semiconductor devices. The International Journal of Advanced Manufacturing Technology. 2022:1-19en_US
dc.identifier.cristinIDFRIDAID 2030882
dc.identifier.doi10.1007/s00170-022-08949-z
dc.identifier.issn0268-3768
dc.identifier.issn1433-3015
dc.identifier.urihttps://hdl.handle.net/10037/25948
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.journalThe International Journal of Advanced Manufacturing Technology
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleStatistical supervised learning with engineering data: a case study of low frequency noise measured on semiconductor devicesen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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