Machine learning based recursive partitioning for simplifying OPC model building complexity

dc.contributor.author	Oak, Apoorva
dc.contributor.author	Hwang, Soobin
dc.contributor.author	Chen, Ruoxia
dc.contributor.author	Kang, Shinill
dc.contributor.author	Kim, Ryan Ryoung han
dc.contributor.imecauthor	Oak, Apoorva
dc.contributor.imecauthor	Kim, Ryan Ryoung han
dc.contributor.orcidimec	Oak, Apoorva::0000-0002-0926-848X
dc.date.accessioned	2022-06-16T09:46:28Z
dc.date.available	2021-11-02T15:59:40Z
dc.date.available	2022-06-16T09:46:28Z
dc.date.issued	2021
dc.identifier.doi	10.1117/12.2584704
dc.identifier.eisbn	978-1-5106-4062-7
dc.identifier.issn	0277-786X
dc.identifier.uri	https://imec-publications.be/handle/20.500.12860/37770
dc.publisher	SPIE-INT SOC OPTICAL ENGINEERING
dc.source.beginpage	116140O
dc.source.conference	Conference on Design-Process-Technology Co-optimization XV
dc.source.conferencedate	FEB 22-26, 2021
dc.source.conferencelocation	Virtual
dc.source.journal	Proceedings of SPIE
dc.source.numberofpages	8
dc.source.volume	11614
dc.title	Machine learning based recursive partitioning for simplifying OPC model building complexity
dc.type	Proceedings paper
dspace.entity.type	Publication
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