A two-dimensional, cellular automata model for atomic layer etching (ALE) is presented and used to predict the etch rate and the evolution of the roughness of various surfaces as a function of the efficiencies or probabilities of the adsorption and removal steps in the ALE process. The atoms of the material to be etched are initially placed in a two-dimensional
array several layers thick. The etch follows the two step process of ALE. First, the initial reaction step (e.g., Cl reacting with Si) is assumed to occur at 100% efficiency activating the exposed, surface atoms; that is, all exposed atoms react with the etching gas. The second reaction step (e.g., Ar ion bombardment or sputtering) occurs with efficiencies that are
assumed to vary depending on the exposure of the surface atoms relative to their neighbors and on the strength of bombardment. For sufficiently high bombardment or sputtering, atoms below the activated surface atoms can also be removed, which gives etch rates greater than one layer per ALE cycle. The bounds on the efficiencies of the second removal step are extracted from experimental measurements and fully detailed molecular dynamics simulations from the literature. A trade-off is observed between etch rate and surface roughness as the Ar ion bombardment is increased.
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A model-based, Bayesian approach to the CF4/Ar etch of SiO2
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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- Award ID(s):
- 1701121
- NSF-PAR ID:
- 10145301
- Date Published:
- Journal Name:
- Proc. SPIE 10588, Design-Process-Technology Co-optimization for Manufacturability XII
- Volume:
- 105880G
- Page Range / eLocation ID:
- 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2297482
