(716h) Multiscale Computational Fluid Dynamics Modeling of Plasma Enhanced Atomic Layer Deposition

Atomic layer deposition (ALD) has gained extensive research interests in the past two decades, due to its distinguished capability to deliver highly conformal thin-film materials in high aspect ratio structures [1]. As one of the most promising varieties of ALD, plasma enhanced atomic layer deposition (PEALD), which provides distinct advantages for the high-k dielectric material and low operating temperature production, is utilized more and more often for metal-oxide-semiconductor field-effect transistors (MOSFETs) and other modern transistor designs [2]. Nevertheless, due to the lack of appropriate in-situ monitoring method to investigate the plasma species transport in the complicated reactor geometry and their influences on the surface deposition, simulation models have great potential in helping understanding the PEALD processes [3]. Many single domain simulation models have been proposed including the plasma chamber simulation for the plasma generation reaction and the microscopic surface model for deposition mechanism and thin-film structure prediction [3,4].

Nevertheless, although each of the models can reveal important information in its respective domain, a complete picture of the PEALD process needs a gas-phase transport profile in the main reactor geometry to connect the remote plasma chamber and substrate surface. Therefore, in this work, the construction of an multi-scale computational fluid dynamics (CFD) model is discussed, which incorporates and integrates three parts: the remote plasma generation domain, the macroscopic gas-phase transport domain, and the microscopic surface reaction domain. An integrated message passing interface (MPI) is built for the communication between all three domains and for parallel computation. The resulting model is compared with a variety of literature works to demonstrate its validity. Additionally, based on the simulation, an automated workflow and reactor geometry optimization can be implemented.

[1] Schuegraf, K., Abraham, M.C., Brand, A., Naik, M., Thakur, R., 2013. Semiconductor logic technology innovation to achieve sub-10 nm manufacturing. IEEE Journal of the Electron DevicesSociety 1, 66–75.

[2] 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.

[3] Tinck, S., Bogaerts, A., 2011. Computer simulations of an oxygen inductively coupled plasma used for plasma-assisted atomic layer deposition. Plasma Sources Science and Technology 20, 015008. nm manufacturing. IEEE Journal of the Electron DevicesSociety 1, 66–75.

[4] 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.