Characterization of Capacitively-Coupled Argon Plasma at Moderate Pressure using Fluid-MCS Hybrid Model
ORAL
Abstract
Radio-frequency (RF) capacitively-couple plasma (CCP) in pure Argon at the moderate pressure regime (a few Torr) is characterized using a one-dimensional fluid-MCS hybrid plasma model. In this regime, the collisional mean free path is generally smaller than the domain size, hence a fluid model is appropriate to the characterize the bulk plasma. The model includes continuity equations for charged and neutral species, drift-diffusion approximation for electron flux, the momentum conservation equation for ions, and the Poisson equation for electric potential. Secondary electrons emitted from the electrode surfaces accelerate across the sheath, gaining significant amount of energy. A Monte Carlo model for secondary electrons is used to accurately compute production rates of species, which are coupled to the fluid plasma model. As these electrons reach an energy level below a threshold, they are merged to the bulk electrons. At low pressures, these high energy beam electrons can exit the domain at the opposite electrode without any significant heavy particle collisions. At high pressures, secondary electrons undergo collisions, losing their energy to the plasma and equilibrating with bulk electrons. At moderate pressures, the behavior is more complex and needs to be modeled accurately. In this study, the role of the secondary electrons on the plasma dynamics at the moderate pressure is demonstrated. The spatio-temporal characteristics of the plasma are illustrated for different secondary electron emission coefficients. The voltage-current characteristics of the plasma model are corroborated using electrical measurements in a symmetric CCP.
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Presenters
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Rupali Sahu
Applied Materials
Authors
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Rupali Sahu
Applied Materials
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Nakul Nuwal
Applied Materials, Inc., Applied Materials
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Kallol Bera
Applied Materials, Inc., Applied Materials
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Shahid Rauf
Applied Materials, Inc., Applied Materials
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Jean-Paul Booth
LPP-CNRS
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Shu Zhang
LPP-CNRS
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Garrett Curley
LPP-CNRS