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Novel concept-oriented synthetic data approach for training generative AI-Driven crystal grain analysis using diffusion model

 
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cris.virtual.orcid0000-0002-2663-1922
cris.virtual.orcid0000-0002-3955-0638
cris.virtual.orcid0000-0003-3822-5953
cris.virtual.orcid0000-0002-0290-691X
cris.virtualsource.departmente31fa604-dc86-4079-912d-679d8185648f
cris.virtualsource.departmente5db7419-6810-435c-9c41-67ff0eeb4bc3
cris.virtualsource.department99f46578-0b77-4a3f-b8e5-a6879cd2ea9a
cris.virtualsource.department60497238-bd25-43d2-a0aa-de0269427c92
cris.virtualsource.orcide31fa604-dc86-4079-912d-679d8185648f
cris.virtualsource.orcide5db7419-6810-435c-9c41-67ff0eeb4bc3
cris.virtualsource.orcid99f46578-0b77-4a3f-b8e5-a6879cd2ea9a
cris.virtualsource.orcid60497238-bd25-43d2-a0aa-de0269427c92
dc.contributor.authorSaleh, Ahmed
dc.contributor.authorCroes, Kristof
dc.contributor.authorCeric, H.
dc.contributor.authorDe Wolf, Ingrid
dc.contributor.authorZahedmanesh, Houman
dc.contributor.imecauthorSaleh, A. S.
dc.contributor.imecauthorCroes, K.
dc.contributor.imecauthorDe Wolf, I.
dc.contributor.imecauthorZahedmanesh, H.
dc.date.accessioned2025-02-25T22:12:55Z
dc.date.available2025-02-25T22:12:55Z
dc.date.issued2025-MAR
dc.description.wosFundingTextThe authors extend their sincere gratitude to Marleen van der Veen (Principal Member of Technical Staff at imec, Belgium) for generously providing the TEM images for Ruthenium (Ru) interconnects. Her support and contributions are greatly appreciated. The authors would also express their appreciation for the help provided by Olalla Varela Pedreira (Quality and Reliability Testing at imec, Belgium) and Patrick Carolan (Senior Process engineer imec, Belgium) during the preparation of the TEM images for Copper (Cu) interconnects. This work has been enabled in part by the NanoIC pilot line. The acquisition and operation are jointly funded by the Chips Joint Undertaking, through the European Union's Digital Europe (101183266) and Horizon Europe programs (101183277), as well as by the participating states Belgium (Flanders), France, Germany, Finland, Ireland and Romania. For more information, visit nanoic-project.eu.
dc.identifier.doi10.1016/j.commatsci.2025.113723
dc.identifier.issn0927-0256
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/45258
dc.publisherELSEVIER
dc.source.beginpage113723
dc.source.issueMarch
dc.source.journalCOMPUTATIONAL MATERIALS SCIENCE
dc.source.numberofpages9
dc.source.volume251
dc.subject.keywordsMICROSTRUCTURE
dc.subject.keywordsSEGMENTATION
dc.subject.keywordsSIMULATION
dc.title

Novel concept-oriented synthetic data approach for training generative AI-Driven crystal grain analysis using diffusion model

dc.typeJournal article
dspace.entity.typePublication
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