A study of a kinetic rate equation model for simulations of MBE crystal growth: A comparison with Monte Carlo simulations

被引:2
|
作者
Papajova, D
Nemeth, S
Hagston, WE
Sitter, H
Vesely, M
机构
[1] UNIV HULL, DEPT APPL PHYS, KINGSTON HULL HU6 7RX, N HUMBERSIDE, ENGLAND
[2] JOHANNES KEPLER UNIV, INST EXPTL PHYS, FESTKORPERPHYS ABT, A-4040 LINZ, AUSTRIA
关键词
computer simulation; molecular beam epitaxy; surface morphology;
D O I
10.1016/0040-6090(95)06631-4
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We present the simulations of molecular beam epitaxy (MBE) growth using a rate equation (RE) model and its comparison with Monte-Carlo (MC) simulations. The advantage of the RE model is the higher speed of calculations, so a much shorter time is required for obtaining results, The RE model is described by a set of differential equations that calculate at each time interval the change of the N-kj numbers of atoms and islands of each k size in each jth layer. This change is due to kinetic processes occurring on the surface during the growth. In the original model (R. Kariotis and H.G. Lagally, Surf Sci., 216 (1989) 557) the probabilities of these processes were described by parameters (input parameters for equations) and the simulations of MBE growth were realized by an appropriate choice of them. To make this model applicable to real simulations, we have included the substrate-temperature dependence of all input parameters using an Arrhenius form, This form is used in MC simulations to calculate a migration of atoms on the surface with substrate-temperature dependence. Since the RE model is described by a set of differential equations it was important to first find the allowed temperature range for simulations. This range includes the substrate temperature for the 3D growth mode (low temperatures) and also for the 2D growth mode (epitaxial temperatures). Using an Arrhenius form for temperature dependence of the parameters in the RE model we were able to compare the obtained results with MC calculations. We have made MC simulations (S, Nemeth, R. Harman and M. Vesely;, Correlation between the stochastic simulation of molecular beam epitaxy growth and experiment, 9th Int. Conf. of Thin Films, 6-10 September, 1993, Vienna, Austria) using the same input parameters (T = 775 K, E(n) = 0.3 eV, E(s) = 1.45 eV). Since the RE model is strongly substrate-size dependent (D. Papajova, W.E. Hagston and P. Harrison, Appl. Phys. A, 59 (1994) 215-222; D. Papajova, S. Nemeth, W.E Hagston, H. Sitter and M. Vesely, J.Appl. Phys. A, submitted) we have found very good agreement in 2D growth for smaller substrate sizes S (in the RE model) only, when this dependence does not influence the results.
引用
收藏
页码:47 / 50
页数:4
相关论文
共 50 条
  • [21] Conditions for organized nanoring growth using kinetic Monte Carlo simulations
    Dumont, F.
    Picaud, F.
    Ramseyer, C.
    Girardet, C.
    PHYSICAL REVIEW B, 2008, 77 (15):
  • [22] Kinetic Monte-Carlo simulations of germanium epitaxial growth on silicon
    Richard Akis
    David Ferry
    Journal of Computational Electronics, 2006, 5 : 451 - 454
  • [23] Kinetic Monte Carlo simulations of epitaxial growth of wurtzite GaN(0001)
    Chugh, Manjusha
    Ranganathan, Madhav
    PHYSICA STATUS SOLIDI C: CURRENT TOPICS IN SOLID STATE PHYSICS, VOL 12, NO 4-5, 2015, 12 (4-5): : 408 - 412
  • [24] Direct Kinetic Monte Carlo Simulations of Interdiffusion
    Sowa, P.
    Kozubski, R.
    Murch, G. E.
    Belova, I. V.
    JOURNAL OF PHASE EQUILIBRIA AND DIFFUSION, 2025, 46 (01) : 186 - 203
  • [25] Lattice gas models and kinetic Monte Carlo simulations of epitaxial growth
    Biehl, M
    MULTISCALE MODELING IN EPITAXIAL GROWTH, 2005, 149 : 3 - 18
  • [26] Detailed Kinetic Monte Carlo Simulations of Graphene-Edge Growth
    Whitesides, Russell
    Frenklach, Michael
    JOURNAL OF PHYSICAL CHEMISTRY A, 2010, 114 (02): : 689 - 703
  • [27] Kinetic Monte-Carlo simulations of germanium epitaxial growth on silicon
    Akis, Richard
    Ferry, David
    JOURNAL OF COMPUTATIONAL ELECTRONICS, 2006, 5 (04) : 451 - 454
  • [28] Kinetic Monte Carlo simulations with minimal searching
    Schulze, TP
    PHYSICAL REVIEW E, 2002, 65 (03): : 1 - 036704
  • [29] Kinetic Monte Carlo simulations of organic ferroelectrics
    Cornelissen, Tim D.
    Biler, Michal
    Urbanaviciute, Indre
    Norman, Patrick
    Linares, Mathieu
    Kemerink, Martijn
    PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2019, 21 (03) : 1375 - 1383
  • [30] Monte Carlo Methods for Reactor Kinetic Simulations
    Srivastava, Argala
    Singh, K. P.
    Degweker, S. B.
    NUCLEAR SCIENCE AND ENGINEERING, 2018, 189 (02) : 152 - 170