Molecular dynamics simulation of aluminum nitride deposition: temperature and N : Al ratio effects

Zhang, Libin; Yan, Han; Zhu, Guo; Liu, Sheng; Gan, Zhiyin

doi:10.1098/rsos.180629

Cited by 16 publications

(6 citation statements)

References 39 publications

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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: 97%

“…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%

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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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Section: Resultsmentioning

confidence: 97%

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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“…It has been extensively used to study nitride deposition processes. In particular, Zhang et al , employed to investigate the effect of substrates on AlGaN film growth and the effect of temperature and N/Al ratio on GaN deposition. Liang et al simulated and analyzed the effect of substrate rotation speed and incidence angle of the injection source on the growth of GaN.…”

Section: Introductionmentioning

confidence: 99%

Crystal Structure and Surface Morphology of GaN Thin Films Grown on Different Patterned AlN Substrate Surfaces

Mao,

Gao,

et al. 2024

Crystal Growth & Design

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Gallium nitride (GaN) is frequently used as the primary material for manufacturing high-brightness light-emitting diodes (LEDs). Recently, atomic layer deposition has become increasingly prevalent across microelectronics, optoelectronics, and nanotechnology as a method for thin-film growth. Gaining insights into the process of thin-film growth can significantly enhance the device performance. Herein, molecular dynamics was used to simulate GaN thin film growth on substrates with different patterned surfaces. Results showed that GaN thin films grown on cone- and dome-patterned substrate surfaces have smoother surfaces than those grown on a flat substrate. Moreover, the GaN thin films grown on flat surfaces exhibited an optimal crystalline quality compared to those grown on patterned substrate surfaces. However, the GaN thin films grown on flat surfaces showed strain levels higher than those grown on patterned substrate surfaces, potentially affecting the performance of the optoelectronic devices. This study reveals the impact of different patterned substrate surfaces on GaN thin films and provides guidance on the design of substrate patterns for different GaN thin film application scenarios.

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“…For example, John et al [ 23 ] used density functional theory to study the microstructure and electrical properties of all-nitrogen interlayers, and the density of states revealed the causes of metallicity and rigidity in the interlayer. Zhang et al [ 24 ] studied the growth mechanism of AlN by molecular dynamics. The effects of different N:Al fluxes ratios, temperatures, and stresses on the crystallinity of AlN were analyzed, and the environmental conditions of the best crystallization quality and the location of defects were obtained.…”

Section: Introductionmentioning

confidence: 99%

Directly Controlling the Transport Properties of All-Nitride Josephson Junctions by N-Vacancy Defects

Qiu¹,

Sun²,

Wang³

et al. 2023

Nanomaterials

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All-nitride Josephson junctions are being actively explored for applications in superconducting quantum chips because of their unique advantages including their antioxidant chemical stability and high crystal quality. However, the theoretical research on their microstructure mechanism that determines transport properties is still absent, especially on the defects. In this paper, we apply the first principles and non-equilibrium Green’s function to calculate the electrical transport characteristics of the yellow preset model. It is first revealed that the N-vacancy defects play a crucial role in determining the conductivity of the NbN-based Josephson junctions, and demonstrate the importance for the uniformity of vacancy distribution. It is found that the uniform number of vacancies can effectively increase the conductance of Josephson junction, but the position distribution of vacancies has little effect on the conductance. The work clarifies the effect of the N-vacancy defects on the conductivity of the NbN-based Josephson junctions, which offers useful guidance for understanding the microscope mechanism of the NbN-based Josephson junction, thus showing a great prospect in the improvement of the yield of superconducting quantum chips in the future.

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Molecular dynamics simulation of aluminum nitride deposition: temperature and N : Al ratio effects

Cited by 16 publications

References 39 publications

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

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

Crystal Structure and Surface Morphology of GaN Thin Films Grown on Different Patterned AlN Substrate Surfaces

Directly Controlling the Transport Properties of All-Nitride Josephson Junctions by N-Vacancy Defects

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