2014
DOI: 10.1117/12.2047827
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Impact of stochastic effects on EUV printability limits

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Cited by 23 publications
(13 citation statements)
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“…However, the well know tradeoff of sensitivity, roughness and resolution (RLS) continues to be a huge challenge for EUV lithography. Conventional chemically amplified resists (CAR) have advantages of mature manufacturing compatibility, however higher dose is often needed to reduce photon shot noise effects and to improve LWR or LCDU [1][2][3]. LWR and LCDU of state-of-art EUV resists are still far from manufacturing requirements.…”
Section: Introductionmentioning
confidence: 99%
“…However, the well know tradeoff of sensitivity, roughness and resolution (RLS) continues to be a huge challenge for EUV lithography. Conventional chemically amplified resists (CAR) have advantages of mature manufacturing compatibility, however higher dose is often needed to reduce photon shot noise effects and to improve LWR or LCDU [1][2][3]. LWR and LCDU of state-of-art EUV resists are still far from manufacturing requirements.…”
Section: Introductionmentioning
confidence: 99%
“…In EUV no assist features are used, OPC is much less aggressive with (almost) no litho-etch bias and, most important, the OPC solution is the same for identical features. This, in combination with stochastic effects in EUV due to shot noise [3], results in failures that can appear randomly. Therefore an EUV-hotspot mostly refers to a type of feature that is likely to fail and not to an exact location.…”
Section: ) "Stochastic" Effectsmentioning
confidence: 99%
“…Thus, the image formation in EUV projection systems has specific challenges not found in immersion lithography. A number of authors have investigated EUV specific effects such as pattern placement error [1], assist feature placement and resulting Bossung tilt [2] and the stochastic nature of EUV imaging [3]. The recent publication by Hsu et al [4] demonstrated resolution enhancement techniques (RET) to address these EUV specific issues as part of the ASML Brion Tachyon NXE Source Mask Optimization (SMO) application.…”
Section: Introductionmentioning
confidence: 98%