2009
DOI: 10.1117/12.829657
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Extensions of boundary layer modeling of photomask topography effects to fast-CAD using pattern matching

Abstract: In lithography for the 45nm node and beyond, phase errors introduced through electromagnetic field (EMF) effects at photomask openings are significant sources of error in calculating on-wafer images. These edge effects create distortion in both real and imaginary field transmission, which leads to a tilt in the process window, and must be addressed in mask design to avoid loss of process latitude. This study presents a new formulation for pattern matching, which allows EMF effects to be included via boundary l… Show more

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Cited by 2 publications
(4 citation statements)
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“…(Color online) Kernel convolution method with simulationgenerated kernels for estimating through-focus behavior of mask edge effects. 37)…”
Section: Discussionmentioning
confidence: 99%
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“…(Color online) Kernel convolution method with simulationgenerated kernels for estimating through-focus behavior of mask edge effects. 37)…”
Section: Discussionmentioning
confidence: 99%
“…Mask edge effects that alter the real part of the transmitted electric field change the effective width of the opening and can be corrected by a bias but effects that alter the imaginary part of the transmitted electric field tilt the through-focus behavior and degrade the process window. 28) Multilayer Mo and Si masks for extreme ultra violet (EUV) lithography at 13.5 nm use a smoothing deposition process that reduces the bump height to a few nm but spreads the bump to a diameter of about 60 nm. Michael Lam used FDTD to first carefully characterize the nearly undeflected forward scattering, the ray like tilted surface scattering in the backward direction and the resonate like effects for new waves created at various heights within the multilayer stack.…”
Section: Mask Edge Effectsmentioning
confidence: 99%
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“…6 Here we summarize and revise this theory. The algorithm is based on using the point response of a single mask pixel to estimate the intensity at a specific location.…”
Section: Kernel Convolution With Pattern Matchingmentioning
confidence: 96%