Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/153903
DC Field | Value | |
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dc.title | DEVELOPING A FAST IMAGING ALGORITHM USING DECOMPOSITION METHODS | |
dc.contributor.author | CHEN AO | |
dc.date.accessioned | 2019-05-09T05:30:37Z | |
dc.date.available | 2019-05-09T05:30:37Z | |
dc.date.issued | 2003 | |
dc.identifier.citation | CHEN AO (2003). DEVELOPING A FAST IMAGING ALGORITHM USING DECOMPOSITION METHODS. ScholarBank@NUS Repository. | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/153903 | |
dc.description.abstract | Aerial image computation has become a crucial component for Optical Proximity Correction (OPC) in microlithography. The currently used partially coherent illumination has caused intensive computation and inadequate speed. Based on the newly-developed VDER technique, a fast imaging algorithm is described using decomposition methods in the spatial domain. Truncations of the expansion employed leads to great computational advantages. Examples are then presented in which GDS masks are loaded and aerial image profiles are computed and displayed for given illumination and projection system parameters. The proposed algorithm proves to be fast and viable. | |
dc.source | SMA BATCHLOAD 20190422 | |
dc.subject | Aerial Image Computation | |
dc.subject | Geometrical Diffraction Theory | |
dc.subject | VDER | |
dc.subject | Optical Proximity Correction | |
dc.subject | Mercer Expansion | |
dc.subject | Tcl/Tk | |
dc.type | Thesis | |
dc.contributor.department | SINGAPORE-MIT ALLIANCE | |
dc.contributor.supervisor | ANDREW KHOH | |
dc.contributor.supervisor | CHUA SOO JIN | |
dc.description.degree | Master's | |
dc.description.degreeconferred | MASTER OF SCIENCE IN ADVANCED MATERIALS FOR MICRO- & NANO- SYSTEMS | |
dc.description.other | Dissertation Supervisors: 1. Mr. Andrew Khoh, Chartered Semiconductor Manufacturing. 2. Prof Chua Soo Jin, SMA Fellow, National University of Singapore. | |
Appears in Collections: | Master's Theses (Restricted) |
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CHEN AO_Thesis.pdf | 317.3 kB | Adobe PDF | RESTRICTED | None | Log In |
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