(378f) Feature Profile Evolution: From Plasma Etching and Deposition to Surface Roughness Propagation | AIChE

(378f) Feature Profile Evolution: From Plasma Etching and Deposition to Surface Roughness Propagation


Hoang, J. - Presenter, University of California at Los Angeles

The limit of current integrated circuit device sizes is defined by state of the art processing technology, including the interplay between photolithography and patterning transferring by plasma etching. These two processes have a convoluted relation among complex surface kinetics, physical dependencies, and gas phase flux distributions that define the evolution of surface features. In this work, a model is developed to investigate the feature profile evolution during deposition and etching with a focus on roughness formation and its propagation. Surface kinetics is based on a translated mixed layer model develop by Kwon et. al. 1 and is implemented in a 3D Monte Carlo simulation domain. Ion incident angle dependence and an elliptical energy deposition model were used to capture the effects of surface morphology on the profile evolution under the bombardment of energetic and directional ions. Species fluxes are determined from experiments or through a reactor scale model. 2 Specifically, we examine a chlorine-based plasma etching and how passivating species affect roughness formation through modifying the local surface composition. A translated mixed layer kinetics model is fitted to chlorine plasma beam etching experiments on silicon dioxide, and the reaction parameters are extracted to determine the relative etch yield on partially oxidized surfaces. Atomic force microscopy measurements of chlorined plasma etched Si with varying amounts of O2 addition in the feed gas are compared to the simulated roughness and show qualitatively good agreement. For ionized deposition, we investigate the effects of roughness and geometry on the deposition conformality. The directionality of the ions along with the extent of physical sputtering is investigated and extracted from experimental SEM images. These parameters are then incorporated into the feature scale model, where the effects of propagation and geometry are investigated and show reasonable agreement with the observed SEM images.

1 Kwon et al. Journal of Vacuum Science and Technology A. 24(5) 2006

2 Hsu et al. Journal of Vacuum Science and Technology B. 26 (6) 2008