Приказ основних података о документу

dc.creatorMladenović, Ivana
dc.creatorLamovec, Jelena
dc.creatorJović, Vesna
dc.creatorObradov, Marko
dc.creatorRadulović, Katarina
dc.creatorVasiljević-Radović, Dana
dc.creatorRadojević, Vesna
dc.date.accessioned2019-12-14T17:23:13Z
dc.date.available2019-12-14T17:23:13Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-3419-2
dc.identifier.urihttps://cer.ihtm.bg.ac.rs/handle/123456789/3227
dc.description.abstractCopper coatings are produced on silicon wafer by electrodeposition (ED) for various cathode current densities. The resulting composite systems consist of 10 μm monolayered copper films electrodeposited from sulphate bath on Si wafers with sputtered layers of Cr/Au. Hardness measurements were performed to evaluate properties of the composites. The composite hardness (H c ) was characterized using Vickers microindentation test. Then, an artificial neural network (ANN) model was used to study the relationship between the parameters of metallic composite and their hardness. Two experimental values: applied load during indentation test and current density during the ED process were used as the inputs to the neural network. Finally, the results of the composite hardness (experimental and predicted) were used to estimate the film hardness (H f ) of copper for each variations of the current density. This article shows that ANN is an useful tool in modeling composite hardness change with variation of experimental parameters predicting hardness change of composite Si/Cu with average error of 6 %. Using created ANN model it is possible to predict microhardness of Cu film for current density or indentation load for which we do not have experimental data.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/32008/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/34011/RS//
dc.rightsrestrictedAccess
dc.source2019 IEEE 31st International Conference on Microelectronics, MIEL 2019 - Proceedings
dc.subjectFilms
dc.subjectArtificial neural networks
dc.subjectPredictive models
dc.subjectCurrent density
dc.subjectSubstrates
dc.subjectLoad modeling
dc.subjectCopper
dc.titleArtificial Neural Network for Composite Hardness Modeling of Cu/Si Systems Fabricated Using Various Electrodeposition Parametersen
dc.typeconferenceObject
dc.rights.licenseARR
dcterms.abstractМладеновић, И.; Радуловић, К.; Радојевић, В.; Обрадов, М.; Јовић, В.; Васиљевић-Радовић, Дана; Ламовец, Ј.;
dc.rights.holderIEEE
dc.citation.spage133
dc.citation.epage136
dc.identifier.doi10.1109/MIEL.2019.8889610
dc.identifier.scopus2-s2.0-85075344033
dc.type.versionpublishedVersion


Документи

Thumbnail

Овај документ се појављује у следећим колекцијама

Приказ основних података о документу