Kinetic Monte Carlo modeling of oxide thin film growth

Abstract

In spite of the increasing interest in and application of ultrathin film oxide oxides in commercial devices, the understanding of the mechanisms that control the growth of these films at the atomic scale remains limited and scarce. Such a limited understanding stems in no minor part from the fact that most of the available modeling methods are unable to access and robustly sample the nanosecond to second time-scales required to simulate both atomic deposition and surface reorganization at ultrathin films. To contribute to this knowledge gap, here we have combined molecular dynamics and adaptive Kinetic Monte Carlo simulation to study the deposition and growth of oxide materials over an extended timescale of up to approximately 0.5 milliseconds. In our pilot studies we have examined the growth of binary oxide thin films on oxide substrates. We have investigated three scenarios (i) the lattice parameter of both the substrate and thin film are identical (ii) the lattice parameter of the thin film is smaller than the substrate and (iii) the lattice parameter is greater than the substrate. Our calculations allow for the diffusion of ions between deposition events and the identification of growth mechanisms in oxide thin films. We make a detailed comparison with previous calculations. Our results are in good agreement with available experimental results and demonstrate important limitations in former calculations, which fail to sample phase space correctly at the temperatures of interest (typically 300K to 1000K) with self-evident limitations for the representative modeling of thin films growth.

Modeling of Magnetic Films: A Scientific Perspective

“…The character of the trial move holds significance in light of two essential requirements for any Monte Carlo algorithm: ergodicity and reversibility. Ergodicity dictates that all possible states of the system should be accessible, while reversibility necessitates that the transition probability between two states remains invariant, explicitly expressed as Equation (14) manifests the evident reversibility as P(S i → S ’ i ) = P(S i → S ’ i ), where the probability of a spin change is contingent solely upon the initial and final energy [ 30 ].…”

Simulation time analysis of kinetic Monte Carlo algorithmic steps for basic radical (de)polymerization kinetics of linear polymers

“…23,24 These kMC algorithms aim at the exact solution for the rigorous chemical master equation and present a simple but powerful tool to describe the time evolution of chemical processes. On top of that, they can be combined with phenomenological models for (mass) transport mechanisms, e.g., diffusion or dispersion, to obtain a detailed description of the studied processes, examples being modelling of traffic and pedestrian flow, 25,26 film, drop or crystal growth, [27][28][29][30][31] vapor deposition, 32,33 atom diffusion on surfaces, 34 electronic and electrochemical applications, [35][36][37] adsorption and heterogeneous catalysis, [38][39][40][41] polycondensation of sugars, 42 epidemiology, 43 kinetics of nucleobases, 44 protein aggregation, 45 and biological and biochemical systems. [46][47][48] The field of polymer reaction engineering (PRE), being the application area in the present work, is a fertile ground to apply kMC algorithms as well, as polymerization kinetics are affected by variations in chain length, chemical composition, and branch location, and, hence, the polymeric macroscopic properties are affected by distributed macromolecular features.…”

“…On top of that, they can be combined with phenomenological models for (mass) transport mechanisms, e.g. , diffusion or dispersion, to obtain a detailed description of the studied processes, examples being modelling of traffic and pedestrian flow, 25,26 film, drop or crystal growth, 27–31 vapor deposition, 32,33 atom diffusion on surfaces, 34 electronic and electrochemical applications, 35–37 adsorption and heterogeneous catalysis, 38–41 polycondensation of sugars, 42 epidemiology, 43 kinetics of nucleobases, 44 protein aggregation, 45 and biological and biochemical systems. 46–48…”

A Signal‐To‐Noise‐Ratio‐Based Automated Algorithm to accelerate Kinetic Monte Carlo Convergence in Basic Polymerizations

“…Such upgraded population-driven output includes, e.g., microscopic distributed compositional or topological information, which can be linked to macroscopic properties at the application level as relevant for manufacturers and end users. [33][34][35] It is thus not surprising that the kMC method has shown to be relevant to simulate distributed populations, with many examples in several fields such as electronic component design, [36] molecular interface dynamics, [37] heterogeneous catalysis, [38] film growth and crystallization, [39,40] biological population evolution, [41] nucleation kinetics, [42] and particle growth. [43][44][45] An additional field in which the kMC method has demonstrated its capabilities and versatility, and the main focus of the present work, is polymer reaction engineering (PRE).…”