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Privacy preserving machine learning for process control

dc.contributor.authorVerachtert, Wilfried
dc.contributor.authorAshby, Tom
dc.contributor.authorChakroun, Imen
dc.contributor.authorWuyts, Roel
dc.contributor.authorDas, Sayantan
dc.contributor.authorHalder, Sandip
dc.contributor.authorLeray, Philippe
dc.date.accessioned2021-10-31T12:24:49Z
dc.date.available2021-10-31T12:24:49Z
dc.date.issued2021
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/37336
dc.sourceIIOimport
dc.titlePrivacy preserving machine learning for process control
dc.typeProceedings paper
dc.contributor.imecauthorVerachtert, Wilfried
dc.contributor.imecauthorAshby, Tom
dc.contributor.imecauthorChakroun, Imen
dc.contributor.imecauthorWuyts, Roel
dc.contributor.imecauthorDas, Sayantan
dc.contributor.imecauthorHalder, Sandip
dc.contributor.imecauthorLeray, Philippe
dc.contributor.orcidimecDas, Sayantan::0000-0002-3031-0726
dc.contributor.orcidimecHalder, Sandip::0000-0002-6314-2685
dc.source.peerreviewyes
dc.source.beginpage116111F
dc.source.conferenceMetrology, Inspection, and Process Control for Microlithography XXXV
dc.source.conferencedate22/02/2021
dc.source.conferencelocationSan Jose,CA USA
dc.identifier.urlhttps://doi.org/10.1117/12.2584893
imec.availabilityPublished - imec
imec.internalnotesProceedings of SPIE; Vol. 11611


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