Study on the competitive growth mechanism of SiC polytypes using Kinetic Monte Carlo method

Chen, Xuejiang; Zhao, Hao; Ai, Wensen

doi:10.1016/j.jcrysgro.2021.126042

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…where ν 0 is the vibration frequency and ν 0 ¼10 13 Hz, k is the Boltzmann's constant, T is the crystal growth temperature, E d represents the diffusion activation energy required, the interaction energy (ΔE inÀfi ) represents the energy difference between the initial site (E in ) and the final site (E fi ), and otherwise it equals zero. The value of E n (n is initial site or final site) depends on the type and number of neighbors, [24] which can be expressed as…”

Section: Doi: 101002/pssb202200307mentioning

confidence: 99%

“…Diffusion rate

F_{diff}

F_{diff} = ν_{0} \exp (- \frac{E_{normald} + Δ E_{in - fi}}{k T})

where

ν_{0}

is the vibration frequency and

ν_{0}

=10 13 Hz,

k

is the Boltzmann's constant, T is the crystal growth temperature,

E_{normald}

represents the diffusion activation energy required, the interaction energy (

Δ E_{in - fi}

) represents the energy difference between the initial site (

E_{in}

) and the final site (

E_{fi}

), and otherwise it equals zero. The value of

E_{n}

( n is initial site or final site) depends on the type and number of neighbors, [ 24 ] which can be expressed as

E_{n} = J_{i} \sum_{j = 1}^{N N} N_{i j} + J_{i i} \sum_{r = 1}^{N N N} N_{i r}

where i is 1 and 2 for Si and C atom, respectively,

N_{i j}

and

N_{i r}

are the numbers of two different types of neighbor atoms (nearest neighbor and next nearest neighbor) around the central atom, J i is the bond energy between nearest neighbor and central atom, and J ii is the bond energy between the next nearest neighbor and central atom. The bond energies of SiC, SiSi, and CC are 0.75, 0.35, and 0.65 eV, respectively.…”

Section: Polytype Competition Kmc Modelmentioning

confidence: 99%

“…The neighborhood relationship of atoms in this study is consistent with that in the study of Chen. [ 24 ] There are 15 neighbors around the central atom, including 9 neighbors of the same polytype and 6 neighbors of different polytype, and the 9 neighbors of the same polytype include 6 nearest neighbors and 3 next nearest neighbors.…”

Section: Polytype Competition Kmc Modelmentioning

confidence: 99%

“…The flow chart of the competition KMC algorithm is shown in Figure 1 . The lattice sites of the two polytypes (4H and 6H polytypes) are constructed and they are the same as that in the study by Chen et al, [ 24 ] their neighbors are recorded, and their diffusion rates are calculated. The desorption rates are minimal, and desorption behaviors aren't considered in this model.…”

Section: Polytype Competition Kmc Modelmentioning

confidence: 99%

“…Recently, a 3D competitive KMC model has been constructed. [ 24 ] In this model, the competitive growth processes of SiC crystals are simulated with an individual atom as the basic particle, and the effects of growth temperature and Si/C ratio on the early competitive growth of 4H‐SiC and 6H‐SiC crystals are studied. However, the effect of deposition flux on the polytype competitive growth is not studied.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Deposition Flux on Polytypic Competitive Growth of SiC Crystal by Kinetic Monte Carlo Method

Chen

Xia

Zhao

et al. 2022

Physica Status Solidi (b)

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A competitive kinetic Monte Carlo (KMC) model for gas‐phase epitaxy growth of 4H‐SiC and 6H‐SiC crystals is established based on polytype transformation and the Monte Carlo algorithm. The competitive growth processes of the two polytypes are simulated, and the effect of deposition flux on the polytype competition is analyzed. The 4H‐SiC competitive growth is promoted by the increase in deposition rate, and the effect of the Si/C ratio on polytypic competition is suppressed by the high deposition rate. Moreover, competitive growth is affected by deposition flux by changing the ratio of diffusion rate to deposition rate.

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Section: Doi: 101002/pssb202200307mentioning

confidence: 99%

“…Diffusion rate

F_{diff}

F_{diff} = ν_{0} \exp (- \frac{E_{normald} + Δ E_{in - fi}}{k T})

where

ν_{0}

is the vibration frequency and

ν_{0}

=10 13 Hz,

k

is the Boltzmann's constant, T is the crystal growth temperature,

E_{normald}

represents the diffusion activation energy required, the interaction energy (

Δ E_{in - fi}

) represents the energy difference between the initial site (

E_{in}

) and the final site (

E_{fi}

), and otherwise it equals zero. The value of

E_{n}

( n is initial site or final site) depends on the type and number of neighbors, [ 24 ] which can be expressed as

E_{n} = J_{i} \sum_{j = 1}^{N N} N_{i j} + J_{i i} \sum_{r = 1}^{N N N} N_{i r}

where i is 1 and 2 for Si and C atom, respectively,

N_{i j}

and

N_{i r}

Section: Polytype Competition Kmc Modelmentioning

confidence: 99%

Section: Polytype Competition Kmc Modelmentioning

confidence: 99%

Section: Polytype Competition Kmc Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effect of Deposition Flux on Polytypic Competitive Growth of SiC Crystal by Kinetic Monte Carlo Method

Chen

Xia

Zhao

et al. 2022

Physica Status Solidi (b)

Self Cite

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Ab initio study of the mechanism of carbonization of {111} Si-substrate at high temperature

Kalchevski,

Trifonov,

Kolev

et al. 2024

Materials Chemistry and Physics

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Microscopic origins of anisotropy for the epitaxial growth of 3C-SiC (0001) vicinal surface: A kinetic Monte Carlo study

Chen

Feng

2022

Journal of Applied Physics

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In order to investigate the microscopic evolution of the step flow growth process and reveal the microscopic origins of crystalline anisotropy during the epitaxial growth of 3C-SiC (0001) vicinal surface, a three-dimensional Kinetic Monte Carlo model is established, in which Si and C are considered individually. The helicoidal boundary condition is applied to the direction perpendicular to the step, and the periodic boundary condition is used in the direction along the step. Then, the effects of crystalline anisotropy on lateral growth rate, morphologies of step patterns, and growth mode are studied. The results show that the lateral growth rate in [1−210] is larger than that in [Formula: see text], and the zigzag and meandering patterns of step are constructed in [1−210] and [Formula: see text] directions, respectively, which is consistent with the experimental observations. Two possible origins of anisotropy are also revealed: one is the higher concentration of the edge sites of the step and the larger bonding energy in the [1−210] direction and another is the adatom diffusion along the edge of the step. Finally, a larger area of pure step flow growth mode is obtained in the [1−210] direction, which is good for lowering the cost for 3C-SiC epitaxial layers.

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Study on the competitive growth mechanism of SiC polytypes using Kinetic Monte Carlo method

Cited by 6 publications

References 32 publications

Effect of Deposition Flux on Polytypic Competitive Growth of SiC Crystal by Kinetic Monte Carlo Method

Effect of Deposition Flux on Polytypic Competitive Growth of SiC Crystal by Kinetic Monte Carlo Method

Ab initio study of the mechanism of carbonization of {111} Si-substrate at high temperature

Microscopic origins of anisotropy for the epitaxial growth of 3C-SiC (0001) vicinal surface: A kinetic Monte Carlo study

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