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

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

doi:10.1117/12.2297482

20 March 2018 A model-based, Bayesian approach to the CF₄/Ar trench etch of SiO₂

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

Proceedings Volume 10588, Design-Process-Technology Co-optimization for Manufacturability XII; 105880G (2018) https://doi.org/10.1117/12.2297482
Event: SPIE Advanced Lithography, 2018, San Jose, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Meghali Chopra, Sofia Helpert, Rahul Verma, Zizhuo Zhang, Xilan Zhu, and Roger Bonnecaze "A model-based, Bayesian approach to the CF₄/Ar trench etch of SiO₂", Proc. SPIE 10588, Design-Process-Technology Co-optimization for Manufacturability XII, 105880G (20 March 2018); https://doi.org/10.1117/12.2297482

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