Atomistic kinetic Monte Carlo simulation on atomic layer deposition of TiN thin film
[Display omitted] •We made a DFT-based kMC model to study TiN ALD process that uses TiCl4 and NH3.•Growth rate per cycle and Cl contamination at surface were in good agreement with experimental results.•Critical role of dissociated surface Cl atom was suggested with detailed reaction mechanisms.•Our...
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Veröffentlicht in: | Computational materials science 2022-10, Vol.213, p.111620, Article 111620 |
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Sprache: | eng |
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•We made a DFT-based kMC model to study TiN ALD process that uses TiCl4 and NH3.•Growth rate per cycle and Cl contamination at surface were in good agreement with experimental results.•Critical role of dissociated surface Cl atom was suggested with detailed reaction mechanisms.•Our study would help reducing impurities and enhancing film qualities.
The atomic layer deposition (ALD) process of TiN thin films is widely used in microelectronics, but the detailed growth mechanism is still elusive at the atomistic level. In the present computational study, we carry out kinetic Monte Carlo (kMC) simulations on the ALD process using TiCl4 and NH3 precursors. Based on the on-lattice model, we sort out key reactions relevant for the ALD process such as adsorption/desorption of precursors, generation of surface Cl atoms, and evolution of free HCl and Cl2 molecules. The reaction energies considering local environments are calculated at the level of density functional theory (DFT) while the activation barriers are linearly fitted to sampled cases among distinct reaction families. The resulting kMC model produces the temperature-dependent growth rates and the amounts of Cl residues in reasonable agreement with experiments. The detailed growth pathway is discussed based on the simulation results, which underscores the critical role of surface Cl atoms in the ALD process by generating HCl gas molecules. By revealing the atomistic mechanisms in the TiN-ALD process, the present work would help optimize material properties of TiN thin films. |
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ISSN: | 0927-0256 1879-0801 |
DOI: | 10.1016/j.commatsci.2022.111620 |