(346o) Microscopic and Machine Learning Modeling of Film Growth in Plasma Enhanced Atomic Layer Deposition

Plasma enhanced atomic layer deposition (PEALD) is one of the most widely adopted deposition methods used in the semiconductor industry [1]. It is chosen largely due to its superior ability to produce ultra-thin high-k dielectric films, which are needed for the further miniaturization of the microelectronic devices with the pace of the Moore's Law [2]. In contrast to the traditional thermal atomic layer deposition (ALD) method, PEALD allows for high deposition growth per cycle (GPC) under low operating temperature with the help of high energy plasma species. Despite the experimental effort in finding new precursors and plasmas, the detailed surface structures and reaction mechanism in various PEALD processes remain hard to understand because of the limitation of current in-situ monitoring techniques and the deficiency of the first-principles based analysis. There are some PEALD simulation models that attempt to reproduce film features, but are too computationally expensive to capture the surface reaction detail for an industrial scale operation [3] .

Therefore, in this work, an accurate, yet efficient DFT-based kinetic Monte Carlo (kMC) model and an associated machine learning (ML) analysis are proposed to capture the surface detail of the HfO₂ thin-film PEALD using Tetrakis-dimethylamino-Hafnium (TDMAHf) and oxygen plasma. First, Density Functional Theory (DFT) calculations are performed to obtain the key thermodynamic and kinetic parameters. Then, using those computed parameters, a detailed Kinetic Monte-Carlo (kMC) model is constructed to describe the surface reaction mechanism and to account for the structure details like steric hindrance caused by the bulky precursor. After the model is validated by experimental data, a database is generated for the cycle completion time under different operating conditions. A feedforward neural network is then constructed to characterize the relationship between partial pressure, temperature and deposition completion time.

[1] Ishikawa, K., Karahashi, K., Ichiki, T., Chang, J.P., George, S.M., Kessels, W., Lee, H.J., Tinck,S., Um, J.H., Kinoshita, K., 2017. Progress and prospects in nanoscale dry processes: How can we control atomic layer reactions? Japanese Journal of Applied Physics 56, 06HA02.

[2] Joo, J., Rossnagel, S.M., 2009. Plasma modeling of a PEALD system for the deposition of TiO2and HfO2. Journal of Korean Physical Society 54, 1048.

[3] Shirazi, M., Elliott, S.D., 2014. Atomistic kinetic Monte Carlo study of atomic layer deposition derived from density functional theory. Journal of Computational Chemistry 35, 244–259.