Publication:
Novel concept-oriented synthetic data approach for training generative AI-Driven crystal grain analysis using diffusion model
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| cris.virtual.department | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| cris.virtual.orcid | 0000-0002-2663-1922 | |
| cris.virtual.orcid | 0000-0002-3955-0638 | |
| cris.virtual.orcid | 0000-0003-3822-5953 | |
| cris.virtual.orcid | 0000-0002-0290-691X | |
| cris.virtualsource.department | e31fa604-dc86-4079-912d-679d8185648f | |
| cris.virtualsource.department | e5db7419-6810-435c-9c41-67ff0eeb4bc3 | |
| cris.virtualsource.department | 99f46578-0b77-4a3f-b8e5-a6879cd2ea9a | |
| cris.virtualsource.department | 60497238-bd25-43d2-a0aa-de0269427c92 | |
| cris.virtualsource.orcid | e31fa604-dc86-4079-912d-679d8185648f | |
| cris.virtualsource.orcid | e5db7419-6810-435c-9c41-67ff0eeb4bc3 | |
| cris.virtualsource.orcid | 99f46578-0b77-4a3f-b8e5-a6879cd2ea9a | |
| cris.virtualsource.orcid | 60497238-bd25-43d2-a0aa-de0269427c92 | |
| dc.contributor.author | Saleh, Ahmed | |
| dc.contributor.author | Croes, Kristof | |
| dc.contributor.author | Ceric, H. | |
| dc.contributor.author | De Wolf, Ingrid | |
| dc.contributor.author | Zahedmanesh, Houman | |
| dc.contributor.imecauthor | Saleh, A. S. | |
| dc.contributor.imecauthor | Croes, K. | |
| dc.contributor.imecauthor | De Wolf, I. | |
| dc.contributor.imecauthor | Zahedmanesh, H. | |
| dc.date.accessioned | 2025-02-25T22:12:55Z | |
| dc.date.available | 2025-02-25T22:12:55Z | |
| dc.date.issued | 2025-MAR | |
| dc.description.wosFundingText | The 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.doi | 10.1016/j.commatsci.2025.113723 | |
| dc.identifier.issn | 0927-0256 | |
| dc.identifier.uri | https://imec-publications.be/handle/20.500.12860/45258 | |
| dc.publisher | ELSEVIER | |
| dc.source.beginpage | 113723 | |
| dc.source.issue | March | |
| dc.source.journal | COMPUTATIONAL MATERIALS SCIENCE | |
| dc.source.numberofpages | 9 | |
| dc.source.volume | 251 | |
| dc.subject.keywords | MICROSTRUCTURE | |
| dc.subject.keywords | SEGMENTATION | |
| dc.subject.keywords | SIMULATION | |
| dc.title | Novel concept-oriented synthetic data approach for training generative AI-Driven crystal grain analysis using diffusion model | |
| dc.type | Journal article | |
| dspace.entity.type | Publication | |
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