A Comparative Study of Deep-Learning Object Detectors for Semiconductor Defect Detection

dc.contributor.author	Dehaerne, Enrique
dc.contributor.author	Dey, Bappaditya
dc.contributor.author	Halder, Sandip
dc.contributor.imecauthor	Dehaerne, Enrique
dc.contributor.imecauthor	Dey, Bappaditya
dc.contributor.imecauthor	Halder, Sandip
dc.contributor.orcidimec	Dey, Bappaditya::0000-0002-0886-137X
dc.contributor.orcidimec	Halder, Sandip::0000-0002-6314-2685
dc.date.accessioned	2023-04-20T09:03:43Z
dc.date.available	2023-02-24T03:29:09Z
dc.date.available	2023-03-07T08:17:54Z
dc.date.available	2023-04-20T09:03:43Z
dc.date.issued	2022
dc.identifier.doi	10.1109/ICECS202256217.2022.9971022
dc.identifier.eisbn	978-1-6654-8823-5
dc.identifier.issn	na
dc.identifier.uri	https://imec-publications.be/handle/20.500.12860/41138
dc.publisher	IEEE
dc.source.conference	29th IEEE International Conference on Electronics, Circuits and Systems (IEEE ICECS)
dc.source.conferencedate	OCT 24-26, 2022
dc.source.conferencelocation	Glasgow
dc.source.journal	2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
dc.source.numberofpages	2
dc.title	A Comparative Study of Deep-Learning Object Detectors for Semiconductor Defect Detection
dc.type	Proceedings paper
dspace.entity.type	Publication
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