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Conference Paper: Stochastic gradient descent for robust inverse photomask synthesis in optical lithography

TitleStochastic gradient descent for robust inverse photomask synthesis in optical lithography
Authors
KeywordsInverse Imaging
Lithography
Machine Learning
Optical Proximity Correction
Robustness
Stochastic Gradient Descent
Issue Date2010
PublisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349
Citation
Proceedings - International Conference On Image Processing, Icip, 2010, p. 4173-4176 How to Cite?
AbstractOptical lithography is a critical step in the semiconductor manufacturing process, and one key problem is the design of the photomask for a particular circuit pattern, given the optical aberrations and diffraction effects associated with the small feature size. Inverse lithography synthesizes an optimal mask by treating the design as an image synthesis inverse problem. To date, much effort is dedicated to solving it for some nominal process conditions. However, the small feature size also suggests that the effect of process variations is more pronounced. In this paper, we design a mask that is robust against focus variations within the inverse lithography framework. Each iteration involves more computation than a similar method designed for the nominal conditions, but we simplify the task by using stochastic gradient descent, which is a technique from machine learning. Simulation shows that the proposed algorithm is effective in producing robust masks. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/158674
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorJia, Nen_US
dc.contributor.authorLam, EYen_US
dc.date.accessioned2012-08-08T09:00:47Z-
dc.date.available2012-08-08T09:00:47Z-
dc.date.issued2010en_US
dc.identifier.citationProceedings - International Conference On Image Processing, Icip, 2010, p. 4173-4176en_US
dc.identifier.issn1522-4880en_US
dc.identifier.urihttp://hdl.handle.net/10722/158674-
dc.description.abstractOptical lithography is a critical step in the semiconductor manufacturing process, and one key problem is the design of the photomask for a particular circuit pattern, given the optical aberrations and diffraction effects associated with the small feature size. Inverse lithography synthesizes an optimal mask by treating the design as an image synthesis inverse problem. To date, much effort is dedicated to solving it for some nominal process conditions. However, the small feature size also suggests that the effect of process variations is more pronounced. In this paper, we design a mask that is robust against focus variations within the inverse lithography framework. Each iteration involves more computation than a similar method designed for the nominal conditions, but we simplify the task by using stochastic gradient descent, which is a technique from machine learning. Simulation shows that the proposed algorithm is effective in producing robust masks. © 2010 IEEE.en_US
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349en_US
dc.relation.ispartofProceedings - International Conference on Image Processing, ICIPen_US
dc.subjectInverse Imagingen_US
dc.subjectLithographyen_US
dc.subjectMachine Learningen_US
dc.subjectOptical Proximity Correctionen_US
dc.subjectRobustnessen_US
dc.subjectStochastic Gradient Descenten_US
dc.titleStochastic gradient descent for robust inverse photomask synthesis in optical lithographyen_US
dc.typeConference_Paperen_US
dc.identifier.emailLam, EY:elam@eee.hku.hken_US
dc.identifier.authorityLam, EY=rp00131en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/ICIP.2010.5653690en_US
dc.identifier.scopuseid_2-s2.0-78651101185en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78651101185&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage4173en_US
dc.identifier.epage4176en_US
dc.identifier.isiWOS:000287728004056-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridJia, N=34872289800en_US
dc.identifier.scopusauthoridLam, EY=7102890004en_US

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