Kinetic Monte Carlo simulations: an accurate bridge between ab initio calculations and standard process experimental data

Jaraı́z, M.; Rubio, E.; Castrillo, P.; Pelaz, Lourdes; Bailón, L.; Barbolla, J.; Gilmer, George H.; Rafferty, C. S.

doi:10.1016/s1369-8001(00)00013-5

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…4 on challenges), the computational power of 1990s propelled molecular dynamics (MD) as the tool of choice for molecular or atomic scale dynamic simulations at the expense of MC simulation. Further increase in computational power, in conjunction with the need to predict transport, materials properties and structure under realistic conditions, led to the realization that most systems contain multiple length and time scales [36][37][38][39][40][41][42]. As a result, the MC method has recently regained considerable attention in the multiscale modeling community.…”

Section: Introductionmentioning

confidence: 99%

An overview of spatial microscopic and accelerated kinetic Monte Carlo methods

Chatterjee

Vlachos

2007

J Computer-Aided Mater Des

416

400

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The microscopic spatial kinetic Monte Carlo (KMC) method has been employed extensively in materials modeling. In this review paper, we focus on different traditional and multiscale KMC algorithms, challenges associated with their implementation, and methods developed to overcome these challenges. In the first part of the paper, we compare the implementation and computational cost of the null-event and rejection-free microscopic KMC algorithms. A firmer and more general foundation of the null-event KMC algorithm is presented. Statistical equivalence between the null-event and rejection-free KMC algorithms is also demonstrated. Implementation and efficiency of various search and update algorithms, which are at the heart of all spatial KMC simulations, are outlined and compared via numerical examples. In the second half of the paper, we review various spatial and temporal multiscale KMC methods, namely, the coarse-grained Monte Carlo (CGMC), the stochastic singular perturbation approximation, and the τ -leap methods, introduced recently to overcome the disparity of length and time scales and the one-at-a time execution of events. The concepts of the CGMC and the τ -leap methods, stochastic closures, multigrid methods, error associated with coarse-graining, a posteriori error estimates for generating spatially adaptive coarse-grained lattices, and computational speed-up upon coarse-graining are illustrated through simple examples from crystal growth, defect dynamics, adsorption-desorption, surface diffusion, and phase transitions.

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Section: Introductionmentioning

confidence: 99%

An overview of spatial microscopic and accelerated kinetic Monte Carlo methods

Chatterjee

Vlachos

2007

J Computer-Aided Mater Des

416

400

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“…The dopants and defects were treated by three mechanisms: jump, switch, and breakup, based on jump rates. 8,9) The point defects jumped to a neighboring position in orthogonal directions with a fixed distance to the nearest first neighbors in the silicon, called , followed by interaction with another neighboring point defect. The interstitial migration energy E mig,I for jump rates given by eq.…”

Section: Kinetic Monte Carlo Modeling For the Dynamic Annealing Effectmentioning

confidence: 99%

Molecular Dynamics Calculation for Low-Energy Ion Implantation Process with Dynamic Annealing Effect

Kwon

Seo

Kim

et al. 2005

Jpn. J. Appl. Phys.

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In this paper, we present a molecular dynamics (MD) study on a low-energy ion implantation process for nanoscale CMOS (Complementary Metal Oxide Semiconductor) processes. To model the profiles of interstitials and vacancies, the recoil interaction approximation (RIA) was employed, while the kinetic Monte Carlo (KMC) approach was used for modeling the dynamic annealing effect between cascades. The simulation results performed for as-implanted boron profiles were compared with the results of the binary collision approximation (BCA) calculation by UT-MARLOWE as well as with the experimental SIMS data. The simulation revealed that the dynamic annealing effect between cascades is essential for the accurate estimation of defect distribution as well as as-implanted ion distribution. The dynamic annealing effect was carefully investigated for a case of boron implantation with an ion implantation energy of 2 keV, doses of 1×1014 ions/cm2 and 1×1015 ions/cm2, and a dose rate of 1×1012 ions/cm2·s.

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“…11,12 In the work presented here the code has been applied to simulate aluminum deposition and annealing under different experimental conditions, i.e., temperatures, deposition rates, and substrate types. The grain size and morphology of the film resulting from the different conditions are analyzed.…”

Section: Introductionmentioning

confidence: 99%