Atomistic Kinetic Monte Carlo Approach Research Articles

Atomistic investigation on the impact of substitutional Al and Si atoms on the carbon kinetics in ferrite

The pairwise interactions of substitutional solute atom X = Al, Si with interstitial carbon at stable (octahedral) and saddle-point (tetrahedral) positions in body-centered-cubic iron (α-Fe) are computed using density-functional theory. These pairwise interactions are used in atomistic kinetic Monte Carlo approach to simulate carbon internal friction and tracer diffusion measurements in Fe-Si, Fe-Al, and Fe-Al-Si ferritic alloys without any adjusting parameters. The good agreement between the simulated and experimental Snoek relaxation profiles validates the pair interaction model for kinetic simulations. The predicted effect of Al on slowing down carbon diffusion is consistent with previous studies. We highlight a super-cell size effect on the Si-carbon interactions obtained from first principles. Using a carefully tested database, it is shown that the introduction of Si into ferrite only decreases the carbon diffusivity below a critical temperature.

Mesoscale simulations of radiation damage effects in Materials: A SEAKMC perspective

Radiation damage presents a unique challenge for material simulations, with processes ranging from picoseconds to decades in time and nanometers to meters in length. Particularly, the understanding of many processes and fundamental mechanisms at the mesoscale are still lacking. In this paper, the self-evolving kinetic Monte Carlo (SEAKMC) method and its application to study mesoscale defect interaction is presented. SEAKMC is an off-lattice atomistic kinetic Monte Carlo approach which conducts ‘on-the-fly’ saddle point searches (SPS) using selective active volumes. The main components of SEAKMC are examined with focus on the comparative benefits of this method. Future areas of improvement are outlined, including suggestions for general improvements to SPS methods by reducing redundant searching and making predictive initial guesses for point defect migration events. Previous applications of SEAKMC to radiation effects in materials are briefly reviewed. Some potential applications of SEAKMC are given, including discussion of dislocation bias, and the further study of interaction between dislocation loops and point defects, which provide useful insights to understand void swelling, radiation induced segregation, and other important radiation damage processes.

Atomistic Modeling of Pocket Dopant Deactivation and Its Impact on <inline-formula> <tex-math notation="LaTeX">$V_{\textrm {th}}$ </tex-math></inline-formula> Variation in Scaled Si Planar Devices Using an Atomistic Kinetic Monte Carlo Approach

An analysis of pocket dopant deactivation and its impact on $V_{\textrm {th}}$ variation for scaled Si devices using an atomistic kinetic Monte Carlo approach are shown in this paper. B 5 keV, $5\times 10^{13}$ /cm $^{\mathrm {{2}}}+$ As 1 keV, $1 \times 10^{15}$ /cm $^{\mathrm {{2}}}$ implants were used for B pocket deactivation study. An effect of laser annealing (LA) before spike-Rapid Thermal Annealing (RTA) was investigated. In case of B pocket implant, a stable B cluster configuration is changed from $\text{B}_{\mathrm {{3}}}\text{I}$ (>1020 °C) to BI $_{\mathrm {{2}}}$ at spike-RTA temperature $\sim 1020^{\circ }\text{C}$ . BI $_{\mathrm {{2}}}$ is a source of B pocket deactivation with lower temperature than 1020 °C. LA before low-temperature spike-RTA ( $V_{\textrm {th}}$ mismatch figure of merit extracted from Pelgrom plot (Avt) degradation in nFET is shown as spike-RTA temperature is reduced. LA before spike-RTA shows a better short channel effect with lower drain-induced barrier lowering in nFET. LA + spike-RTA at 1000 °C shows better Avt than spike-RTA-only. The difference of pocket deactivation is one of possible important reasons for the higher $V_{\textrm {th}}$ mismatch for nFET than for pFET.

Stability and mobility of small vacancy–solute complexes in Fe–MnNi and dilute Fe–X alloys: A kinetic Monte Carlo study

Manganese and nickel solute atoms in irradiated ferritic steels play a major role in the nanostructural evolution of reactor pressure vessels (RPV), as they are responsible for the formation of embrittling nanofeatures even in the absence of copper. The stability and mobility of small vacancy–solute clusters is here studied with an atomistic kinetic Monte Carlo approach based on ab initio calculations, in order to investigate the influence of Mn and Ni on the early life of small radiation-induced vacancy clusters, and to provide the necessary parameters for advanced object kinetic Monte Carlo simulations of the RPV long-term nanostructural evolution. Migration barriers are obtained by direct ab initio calculations or through a binding energy model based on ab initio data. Our results show a clear immobilizing and stabilizing effect on vacancy clusters as the solute content is increased, whereas the only evident difference between the two solute species is a somewhat longer elongation of the cluster mean free path in the presence of a few Mn atoms.

Further development of large-scale atomistic modelling techniques for Fe–Cr alloys

In this paper we review the current status of our efforts to model the Fe–Cr system, which is a model alloy for high-Cr ferritic–martensitic steels, using large-scale atomistic methods. The core of such methods are semi-empirical interatomic potentials. Here we discuss their performance with respect to the features that are important for an accurate description of radiation effects in Fe–Cr alloys. We describe their most recent improvements regarding macroscopic thermodynamic properties as well as microscopic point-defect properties. Furthermore we describe a new type of large-scale atomistic kinetic Monte Carlo (AKMC) approach driven by an artificial neural network (ANN) regression method to generate the local migration barrier for a defect accounting for the local chemistry around it. The results of the thermal annealing of the Fe–20Cr alloy modelled using this AKMC approach, parameterized by our newly developed potential, were found to be in very good agreement with experimental data. Furthermore the interaction of a 1/2 〈1 1 1〉 screw dislocation with Cr precipitates as obtained from the AKMC simulations was studied using the same potential. In summary, we critically discuss our current achievements, findings and outline issues to be addressed in the near future development.

Atomistic modeling of defect diffusion and interdiffusion in SiGe heterostructures

In this work we present models and simulation results for diffusion in strained SiGe heterostructures. Our approach makes a comprehensive, physically-based, treatment of defect and dopant diffusion, self-diffusion (of Si and Ge) and interdiffusion, including some effects inherent to heterostructures. The models have been implemented in the DADOS code, within the framework of the atomistic kinetic Monte Carlo approach. The parameter set has been calibrated in the whole composition range, from silicon to germanium, both for relaxed and strained conditions, and an illustrative set of experiments has been simulated.

Modelling radiation-induced phase changes in binary FeCu and ternary FeCuNi alloys using an artificial intelligence-based atomistic kinetic Monte Carlo approach

We apply a novel atomistic kinetic Monte Carlo model, which includes local chemistry and relaxation effects when assessing the migration energy barriers of point defects, to the study of the microchemical evolution driven by vacancy diffusion in FeCu and FeCuNi alloys. These alloys are of importance for nuclear applications because Cu precipitation, enhanced by the presence of Ni, is one of the main causes of hardening and embrittlement in reactor pressure vessel steels used in existing nuclear power plants. Local chemistry and relaxation effects are introduced using artificial intelligence techniques, namely a conveniently trained artificial neural network, to calculate the migration energy barriers of vacancies as functions of the local atomic configuration. We prove, through a number of results, that the use of the neural network is fully equivalent to calculating the migration energy barriers on-the-fly, using computationally expensive methods such as nudged elastic bands with an interatomic potential. The use of the neural network makes the computational cost affordable, so that simulations of the same type as those hitherto carried out using heuristic formulas for the assessment of the energy barriers can now be performed, at the same computational cost, using more rigorously calculated barriers. This method opens the way to properly treating more complex problems, such as the case of self-interstitial cluster formation, in an atomistic kinetic Monte Carlo framework.

Modeling arsenic deactivation through arsenic-vacancy clusters using an atomistic kinetic Monte Carlo approach

A comprehensive atomistic model for arsenic in silicon which includes charge effects and is consistent with first-principles calculations for arsenic-vacancy cluster energies has been developed. Emphasis has been put in reproducing the electrical deactivation and the annealed profiles in preamorphized silicon. The simulations performed with an atomistic kinetic Monte Carlo simulator suggest a predominant role of the mobile interstitial arsenic in deactivation experiments and provide a good understanding of the arsenic behavior in preamorphized silicon during annealing.