Ms. Sofia Helpert Profile

Sofia Helpert

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Proceedings Article | 20 March 2018 Presentation + Paper

Minimum reaction network necessary to describe Ar/CF4 plasma etch

Sofia Helpert, Meghali Chopra, Roger Bonnecaze

Proceedings Volume 10589, 105890J (2018) https://doi.org/10.1117/12.2297502

KEYWORDS: Plasma, Autoregressive models, Plasma etching, Databases, Argon, Etching, Data modeling, Plasma systems, Temperature metrology, Particles

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Predicting the etch and deposition profiles created using plasma processes is challenging due to the complexity of plasma discharges and plasma-surface interactions. Volume-averaged global models allow for efficient prediction of important processing parameters and provide a means to quickly determine the effect of a variety of process inputs on the plasma discharge. However, global models are limited based on simplifying assumptions to describe the chemical reaction network. Here a database of 128 reactions is compiled and their corresponding rate constants collected from 24 sources for an Ar/CF₄ plasma using the platform RODEo (Recipe Optimization for Deposition and Etching). Six different reaction sets were tested which employed anywhere from 12 to all 128 reactions to evaluate the impact of the reaction database on particle species densities and electron temperature. Because many the reactions used in our database had conflicting rate constants as reported in literature, we also present a method to deal with those uncertainties when constructing the model which includes weighting each reaction rate and filtering outliers. By analyzing the link between a reaction’s rate constant and its impact on the predicted plasma densities and electron temperatures, we determine the conditions at which a reaction is deemed necessary to the plasma model. The results of this study provide a foundation for determining which minimal set of reactions must be included in the reaction set of the plasma model.

Proceedings Article | 20 March 2018 Presentation + Paper

A model-based, Bayesian approach to the CF4/Ar etch of SiO2

Meghali Chopra, Sofia Helpert, Rahul Verma, Zizhuo Zhang, Xilan Zhu, Roger Bonnecaze

Proceedings Volume 10588, 105880G (2018) https://doi.org/10.1117/12.2297482

KEYWORDS: Etching, Plasma, Autoregressive models, Systems modeling, Calibration, Argon, Data modeling, Ions, Model-based design, Chemistry

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The design and optimization of highly nonlinear and complex processes like plasma etching is challenging and timeconsuming. Significant effort has been devoted to creating plasma profile simulators to facilitate the development of etch recipes. Nevertheless, these simulators are often difficult to use in practice due to the large number of unknown parameters in the plasma discharge and surface kinetics of the etch material, the dependency of the etch rate on the evolving front profile, and the disparate length scales of the system. Here, we expand on the development of a previously published, data informed, Bayesian approach embodied in the platform RODEo (Recipe Optimization for Deposition and Etching). RODEo is used to predict etch rates and etch profiles over a range of powers, pressures, gas flow rates, and gas mixing ratios of an CF4/Ar gas chemistry. Three examples are shown: (1) etch rate predictions of an unknown material “X” using simulated experiments for a CF₄/Ar chemistry, (2) etch rate predictions of SiO₂ in a Plasma-Therm 790 RIE reactor for a CF₄/Ar chemistry, and (3) profile prediction using level set methods.

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