Roles of V/III ratio and mixture degree in GaN growth: CFD and MD simulation study

An, Zhenmei; Xiu, Xiangqian; Zhang, Rong; Xie, Zili; Hua, Xue‐Mei; Liu, Bin; Han, Ping; Gu, Shulin; Shi, Yi; Zheng, Yi

doi:10.1088/1674-1056/22/1/017801

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…Aerodynamic forces acting on flies or birds can be computed using the CFD method. [23] The governing equations are impressible Navier-Stokes equations which are solved using Fluent (of Fluent Inc., USA). SIMPLEC algorithm was used for pressure and velocity coupling.…”

Section: Cfd Methodsmentioning

confidence: 99%

A combined airfoil with secondary feather inspired by the golden eagle and its influences on the aerodynamics

et al. 2019

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Bird flight is a remarkable adaption that has allowed thousands of species to colonize all terrestrial habitats. A golden eagle has impressive flying abilities, such as hovering, perching, preying and attacking. To reveal the flying abilities, avian geometry of a golden eagle was extracted based on noncontact surface measurements using a ROMBER three-dimensional laser scanner. Distributions of a camber line, thickness and a secondary feather line of the extracted point cloud were fitted using convenient analytical expressions. A traditional airfoil was established with the camber line and thickness, then a combined airfoil was constructed by combining the traditional airfoil with a secondary feather. Oscillations of an airfoil as well as rapid pitch up were simplified as a sine wave around the quarter chord axis. Thereafter, both steady and unsteady aerodynamic performances of the airfoil are computed, the influences of the secondary feather on the steady and unsteady aerodynamics were further studied.

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Section: Cfd Methodsmentioning

confidence: 99%

A combined airfoil with secondary feather inspired by the golden eagle and its influences on the aerodynamics

et al. 2019

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“…Existing MD simulations of InGaN growth [21][22][23] and GaN growth [24][25][26][27][28][29][30][31][32] have typically considered smaller systems, containing fewer than $20 000 atoms, consistent with their focus on either nanoscale or atomistic processes. Examples include the studies of interfacial bonding, 21 bulk-nanoclustering, 22 quantum-dot self-assembly, 23 surface diffusion, 27 interfacial crystallization, 29 and adatom absorption/desorption.…”

Section: Introductionmentioning

confidence: 99%

Molecular dynamics studies of defect formation during heteroepitaxial growth of InGaN alloys on (0001) GaN surfaces

Gruber

Zhou

Jones

et al. 2017

Journal of Applied Physics

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We investigate the formation of extended defects during molecular-dynamics (MD) simulations of GaN and InGaN growth on (0001) and ([Formula: see text]) wurtzite-GaN surfaces. The simulated growths are conducted on an atypically large scale by sequentially injecting nearly a million individual vapor-phase atoms towards a fixed GaN surface; we apply time-and-position-dependent boundary constraints that vary the ensemble treatments of the vapor-phase, the near-surface solid-phase, and the bulk-like regions of the growing layer. The simulations employ newly optimized Stillinger-Weber In-Ga-N-system potentials, wherein multiple binary and ternary structures are included in the underlying density-functional-theory training sets, allowing improved treatment of In-Ga-related atomic interactions. To examine the effect of growth conditions, we study a matrix of >30 different MD-growth simulations for a range of In Ga N-alloy compositions (0 ≤ ≤ 0.4) and homologous growth temperatures [0.50 ≤ () ≤ 0.90], where () is the simulated melting point. Growths conducted on polar (0001) GaN substrates exhibit the formation of various extended defects including stacking faults/polymorphism, associated domain boundaries, surface roughness, dislocations, and voids. In contrast, selected growths conducted on semi-polar ([Formula: see text]) GaN, where the wurtzite-phase stacking sequence is revealed at the surface, exhibit the formation of far fewer stacking faults. We discuss variations in the defect formation with the MD growth conditions, and we compare the resulting simulated films to existing experimental observations in InGaN/GaN. While the palette of defects observed by MD closely resembles those observed in the past experiments, further work is needed to achieve truly predictive large-scale simulations of InGaN/GaN crystal growth using MD methodologies.

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“…This slow rate of approach (and hence crystallization) results in the formation of a relatively smooth surface at the interface. Similar results have been observed by others, including that the ratio of N to Al affected crystal growth quality. , …”

Section: Resultsmentioning

confidence: 99%

“…Similar results have been observed by others, including that the ratio of N to Al affected crystal growth quality. 52,53 Lastly, we compared the surface energy of the a-, c-, and mplanes from the MD results. In our previous study, the FLAREgenerated force field was able to calculate the elastic constants and lattice constants of GaN as accurately as those from density functional theory (DFT) calculations and experimental measurements.…”

Section: Effect Of the Crystalmentioning

confidence: 99%

Diffusion-Limited Crystal Growth of Gallium Nitride Using Active Machine Learning

Chen,

Le,

Clancy

2024

Crystal Growth & Design

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Gallium nitride (GaN) is an important semiconductor with properties that make it particularly suitable for high-temperature applications in power systems. Unfortunately, its crystalline synthesis involves an energy-intensive process requiring high temperatures and pressures. A new additive manufacturing process offers a lowertemperature alternative, but little is understood of the molecular-scale mechanisms that drive its crystallization from the melt. Traditional semiempirical force fields within a molecular dynamics (MD) simulation are typically unable to capture bond-making and -breaking, whereas ab initio MD, while more accurate, suffers from high computational expense. This paper uses a machine-learned force field based on the FLARE++ framework to mimic the liquid-phase epitaxy of GaN, simulating the diffusion of nitrogen atoms through liquid gallium to form GaN. We show that nitrogen diffusion through Ga is slow, and relatedly, there is a pronounced tendency for nitrogen to phase-segregate within liquid Ga. This leads to nitrogen being less available to react and form crystalline GaN. As a result, the predicted crystal growth at the melt/crystal interface is extremely slow, as seen experimentally. In this work, we demonstrate the potential of a MLFF to describe complex, multiphase behavior under different conditions. We also uncover the key atomistic-level mechanism of diffusion-limited GaN crystal growth, which is an important step toward further control of the additive manufacturing process.

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Roles of V/III ratio and mixture degree in GaN growth: CFD and MD simulation study

Abstract: An(± S) a)b) , Xiu Xiang-Qian(?•c) b) † , Zhang Rong(Ü J) b) , Xie Zi-Li( gå) b) , Hua Xue-Mei(uÈr) b) , Liu Bin(4 R) b) , Han Ping(¸²) b) , Gu Shu-Lin( Ö ) b) , Shi Yi(-À) b) , and Zheng You-Dou(xk ) b)

Cited by 9 publications

References 36 publications

A combined airfoil with secondary feather inspired by the golden eagle and its influences on the aerodynamics

A combined airfoil with secondary feather inspired by the golden eagle and its influences on the aerodynamics

Molecular dynamics studies of defect formation during heteroepitaxial growth of InGaN alloys on (0001) GaN surfaces

Diffusion-Limited Crystal Growth of Gallium Nitride Using Active Machine Learning

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