Modeling and in Situ Probing of Surface Reactions in Atomic Layer Deposition

Abstract

Atomic layer deposition (ALD) has matured into a preeminent thin film deposition technique by offering a highly scalable and economic route to integrate chemically dissimilar materials with excellent thickness control down to the subnanometer regime. Contrary to its extensive applications, a quantitative and comprehensive understanding of the reaction processes seems intangible. Complex and manifold reaction pathways are possible, which are strongly affected by the surface chemical state. Here, we report a combined modeling and experimental approach utilizing ReaxFF reactive force field simulation and in situ real-time spectroscopic ellipsometry to gain insights into the ALD process of AlO from trimethylaluminum and water on hydrogenated and oxidized Ge(100) surfaces. We deciphered the origin for the different peculiarities during initial ALD cycles for the deposition on both surfaces. While the simulations predicted a nucleation delay for hydrogenated Ge(100), a self-cleaning effect was discovered on oxidized Ge(100) surfaces and resulted in an intermixed AlO/GeO layer that effectively suppressed oxygen diffusion into Ge. In situ spectroscopic ellipsometry in combination with ex situ atomic force microscopy and X-ray photoelectron spectroscopy confirmed these simulation results. Electrical impedance characterizations evidenced the critical role of the intermixed AlO/GeO layer to achieve electrically well-behaved dielectric/Ge interfaces with low interface trap density. The combined approach can be generalized to comprehend the deposition and reaction kinetics of other ALD precursors and surface chemistry, which offers a path toward a theory-aided rational design of ALD processes at a molecular level.

Nanoarchitectonics of Illite-Based Materials: Effect of Metal Oxides Intercalation on the Mechanical Properties

“…This computational strategy avoids the tedious setting of parameters for the bond and nonbonded interactions [ 43 , 44 ]. Therefore, the interaction potential parameters among atoms in the four hybrid structures, except potassium atoms, were based on the ReaxFF force field parameters published by Y. Zheng et al [ 45 ].…”

Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence

“…ReaxFF is parameterized exclusively from quantum chemical calculations, and has been shown to reproduce the potential energy surfaces, structures, and reaction barriers for reactive systems at nearly the accuracy of quantum chemical methods but at costs nearly as low as conventional force fields. Applications of ReaxFF have been reported for a wide range of functional materials, including energetic materials, anodes and electrolytes for fuel cell and battery technologies, metals and metal oxides, metal organic frameworks 2D materials, among others.…”

INDEEDopt: a deep learning-based ReaxFF parameterization framework

“…Initially, ReaxFF interatomic potential has been formulized to model hydrocarbons, and then extended through silica, nitramine-based materials to several aqueous and combustion systems 5 . Currently, the ReaxFF method covers over 50 elements, and is applicable to a wide range of chemical systems that are of interest to materials science community (i.e., 2D materials [6][7][8][9][10][11][12][13] , electrochemistry 14,15 , thin films 16,17 , nanotubes 18,19 , catalysts 20,21 ).…”