15 April 2011 A comprehensive model and method for model parameterization for predicting pattern collapse behavior in photoresist nanostructures
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Pattern collapse has become an issue of increasing importance in semiconductor lithography as the size of critical features continues to shrink. Although models have been proposed to explain the observed pattern collapse behavior, the ability of such models to quantitatively predict the collapse behavior has been limited without significant model fitting to experimental pattern collapse behavior. Such a need to collect extensive collapse data before these models can provide any predictive capability limits their use and in general does not provide further insight into the underlying root causes of the observed behavior in many cases. This is particularly true at small feature sizes for resist lines smaller than approximately 70 nm in width. In this work, a comprehensive pattern collapse model that accounts for both adhesion based pattern failure and elastoplastic deformation-based failure is used. Furthermore, the required model parameters are extracted from basic experiments on the resist materials and substrates themselves without the need for actual patterning experiments. For example, the resist mechanical modulus behavior is determined from simple thin film buckling experiments. The results of these simple tests are quantitatively predictive pattern collapse models for a particular resist-substrate combination that capture complex effects such as the dependence of the collapse behavior on resist film thickness and feature size due to feature size dependent polymer modulus behavior. Application of these models and experimental methods to an experimental resist and comparisons of the model predictions versus actual experimental pattern collapse data are shown and discussed to validate the methodology.
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Wei-Ming Yeh, Wei-Ming Yeh, Richard A. Lawson, Richard A. Lawson, Clifford L. Henderson, Clifford L. Henderson, } "A comprehensive model and method for model parameterization for predicting pattern collapse behavior in photoresist nanostructures", Proc. SPIE 7972, Advances in Resist Materials and Processing Technology XXVIII, 79721X (15 April 2011); doi: 10.1117/12.895112; https://doi.org/10.1117/12.895112

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