Application of artificial neural networks to the identification of weak electrical regions in large area MIM structures

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dc.check.date2023-10-11
dc.check.infoAccess to this article is restricted until 24 months after publication by request of the publisher.en
dc.contributor.authorMuñoz-Gorriz, J.
dc.contributor.authorMonaghan, Scott
dc.contributor.authorCherkaoui, Karim
dc.contributor.authorSuñé, Jordi
dc.contributor.authorHurley, Paul K.
dc.contributor.authorMiranda, Enrique
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidadesen
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2022-07-21T11:50:10Z
dc.date.available2022-07-21T11:50:10Z
dc.date.issued2021-10-11
dc.date.updated2022-07-19T21:01:51Z
dc.description.abstractLarge area metal-insulator-metal (MIM) structures are prone to exhibit weak electrical regions when they are subjected to severe stress conditions. Although the root cause of this problem is hard to identify, it has been attributed to non-uniform oxide thickness, variable dielectric permittivity, correlated defect generation, and fringe effects in capacitors. In this paper, we explore the application of artificial neural networks (ANNs) to the spatial localization of such weak regions. To this end, HfO2-based MIM structures were electrically stressed with the objective of generating a large number of breakdown spots. These spots are statistically distributed both in size and location over the device area. Two-input/two-output ANNs with different number of neurons and hidden layers were assessed with the purpose of identifying the best and simplest option for detecting where the most severe damage occurs. The obtained results are compatible with previous studies based on spatial statistics techniques. The method can be applied to other systems that exhibit multiple localized failure events.en
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades (project TEC2017-84321-C4-4-R); Science Foundation Ireland (AMBER 2 project (12/RC/2278-P2))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid114312en
dc.identifier.citationMuñoz-Gorriz, J., Monaghan, S., Cherkaoui, K., Suñé, J., Hurley, P. K. and Miranda, E. (2021) 'Application of artificial neural networks to the identification of weak electrical regions in large area MIM structures', Microelectronics Reliability, 126, 114312 (7pp). doi: 10.1016/j.microrel.2021.114312en
dc.identifier.doi10.1016/j.microrel.2021.114312en
dc.identifier.endpage7en
dc.identifier.issn0026-2714
dc.identifier.journaltitleMicroelectronics Reliabilityen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/13388
dc.identifier.volume126en
dc.language.isoenen
dc.publisherElsevier Ltd.en
dc.rights© 2021, Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectNeural networksen
dc.subjectPerceptronen
dc.subjectSpatial statisticsen
dc.subjectDielectric breakdownen
dc.subjectReliabilityen
dc.subjectMIMen
dc.titleApplication of artificial neural networks to the identification of weak electrical regions in large area MIM structuresen
dc.typeArticle (peer-reviewed)en
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