Mechanistic simulation of line-edge roughness

Biafore, John J.; Smith, Mark D.; Robertson, Stewart A.; Graves, Trey

doi:10.1117/12.712868

Cited by 22 publications

(10 citation statements)

References 13 publications

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“…Stochastic models provide insight into LWR through the use a lattice structure that arranges molecules like PAG, solvent, polymer, etc into cubes scaled to be around 1 nm in size [18][19] . Monte Carlo methods are used to distribute resist components as well as calculate acid generation and deprotection profiles.…”

Section: Rls Performance Tradeoff Quantification With Resist Simulationmentioning

confidence: 99%

Resist fundamentals for resolution, LER, and sensitivity (RLS) performance tradeoffs and their relation to micro-bridging defects

Rathsack

Nafus

Hatakeyama

et al. 2009

Advances in Resist Materials and Processing Technology XXVI

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High NA immersion and EUV lithography processes are challenged to meet stringent control requirements for the 22 nm node and beyond. Lithography processes must balance resolution, LWR and sensitivity (RLS) performance tradeoffs while scaling resist thickness to 100 nm and below 1-3 . Hardware modules including coat, bake and development seek to enable resist processes to balance RLS limitations. The focus of this paper is to study the fundamentals of the RLS performance tradeoffs through a combination of calibrated resist simulations and experiments.This work seeks to extend the RLS learning through the creation of calibrated resist models that capture the exposure kinetics, acid diffusion properties, deprotection kinetics and dissolution response as a function of PAG loading in a 193 nm polymer system. The calibrated resist models are used to quantify the resolution and sensitivity performance tradeoffs as well as the degradation of resist contrast relative to image contrast at small dimensions.Calibrated resist simulations are capable of quantifying resolution and sensitivity tradeoffs, but lack the ability to model LWR. LWR is challenging to simulate (lattice models) and to measure; due to the dependence on spectral frequency. This paper seeks to use micro-bridging experiments as means to better understand the statistical nature of LWR. Microbridging analysis produces a statistical distribution of "discrete bridging events" that encompasses practical variations across scanner, track and resist. Micro-bridging and LWR experiments are done using a 1.2 NA immersion system on 45 nm space structures (90 nm pitch) as a means to demonstrate the concept, but the methodology can also be used to study EUVL processes as the technology matures. The understanding of the RLS performance tradeoffs enables TEL to develop future hardware and processes that support industry scaling goals.

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Section: Rls Performance Tradeoff Quantification With Resist Simulationmentioning

confidence: 99%

Resist fundamentals for resolution, LER, and sensitivity (RLS) performance tradeoffs and their relation to micro-bridging defects

Rathsack

Nafus

Hatakeyama

et al. 2009

Advances in Resist Materials and Processing Technology XXVI

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“…As technology node goes beyond 65-nm-node, however, it seems that optimum SRAF configuration for CD margin window does not coincide with that for the best pattern profile or its representative, LER, anymore. While CD margin window mainly depend on width of intensity profile at a specific threshold level, optical response of LER has found to vary with intensity profile sharpness or image log slope (ILS) 6 as,…”

Section: Introductionmentioning

confidence: 99%

Impact of assistance feature to pattern profile for isolated feature in sub-65 nm node

et al. 2008

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Sub-resolution assistance feature (SRAF) has become one of popular resolution enhancement technique because it is the most easily applicable technique that can be adopted for sub-65 nm node technology. The SRAF can be realized, for example, by locating lines having width below resolution limit around isolated feature. With the SRAF, intensity profile of the isolated feature will be modified to dense-like one and, as a result, focus response of the isolated feature can be improved up to dense feature level. Previous works on SRAF have focused mainly on the critical dimension (CD) margin window. However, CD margin window is not sufficient to evaluate optimum SRAF configuration because process margin degradation due to irregular pattern profile such as line edge roughness (LER) would become more prominent as technology node goes beyond sub-65nm node. Therefore, appropriate methodology to optimize SRAF configuration both for CD margin window and pattern profile is indispensable for those applications.In this paper, we focus on the impact of SRAF configuration to pattern profile as well as CD margin window. The SRAF configuration was adjusted by varying assistance feature to main feature distance and pitch of the assistance features at mask level. Pattern profile was investigated by measuring LER with varying assistance feature parameters quantitatively. From the results, we prove the impact of SRAF configuration both on pattern profile and CD margin window. We also show that the experimental data can easily be predicted by calibrating aerial image simulation results to measured LER. As a conclusion, we suggest methodology to set up optimum SRAF configuration with regard to both CD margin window and pattern profile.

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“…In recent years, there have been many efforts at simulating photoresists on the molecular level 1,2,4,5 . Such mesoscale models attempt to incorporate fundamental polymer physics into a model that simulates the behavior of individual polymer chains and typical photoresist components.…”

Section: Introduction To Mesoscale Modelingmentioning

confidence: 99%

Extension of 248 nm Monte Carlo, mesoscale models to 193 nm platforms

et al. 2010

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Current minimum feature sizes in the microelectronics industry dictate that molecular interactions affect process fidelity and produce stochastic excursions like line edge roughness (LER). The composition of future resists is still unknown at this point, and so simulation of various resist platforms should provide useful information about resist design that minimizes LER. In the past, researchers developed a mesoscale model 1, 2 for exploring representative 248 nm resist systems through dynamic Monte Carlo methods and adaptation of critical ionization theory 3 . This molecular modeling uses fundamental interaction energies combined with a Metropolis algorithm to model the full lithographic process (spin coat, PAB, exposure, PEB, and development). Application of this model to 193 nm platforms allows for comparison between 248 and 193 nm resist systems based on molecular interactions. This paper discusses the fundamental modifications involved in adapting the mesoscale model to a 193 nm platform and investigates how this new model predicts well-understood lithographic phenomena including the relationship between LER and aerial image, the relationship between LER and resist components, and the impact of non-uniform PAG distribution in the resist film. Limited comparisons between the 193 nm system and an analogous 248 nm platform will be discussed.

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Mechanistic simulation of line-edge roughness

Cited by 22 publications

References 13 publications

Resist fundamentals for resolution, LER, and sensitivity (RLS) performance tradeoffs and their relation to micro-bridging defects

Resist fundamentals for resolution, LER, and sensitivity (RLS) performance tradeoffs and their relation to micro-bridging defects

Impact of assistance feature to pattern profile for isolated feature in sub-65 nm node

Extension of 248 nm Monte Carlo, mesoscale models to 193 nm platforms

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