Massive-scale molecular dynamics of ion-irradiated III–V compound semiconductors at the onset of nanopatterning

Lively, Michael; Holybee, B.; Toriyama, Michael Y.; Allain, Jean Paul

doi:10.1016/j.nimb.2017.04.047

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Previous modeling work on GaN crystallization has focused on understanding CVD and molecular beam epitaxy processes, not solidification from the melt. , In addition, there has been no atomic-scale modeling of adatom diffusion on a substrate surface due to the limited force fields capable of modeling both the bulk/melt regions and adequately representing all the different phases and atom-types involved in the process. Even though there has been previous work studying the crystallization of liquid semiconductors and associated energy changes, the system was constrained to bulk phase solidification from liquid to crystal. − Therefore, there remains a need to understand the reaction mechanism of additive manufacturing processes for semiconductors with an accurate and transferable force field capable of representing different phases.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

Chen,

Le,

Clancy

2024

Crystal Growth & Design

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“…Numerous previous studies on material doping and modification by using experimental methods and model simulations have shown that the collision cascade in damage generation is one of the most complex processes of defect physics [5][6][7][8][9]. Using a pulsed ion beam and fractal theory, Wallace et al systematically studied collision cascades in crystal silicon.…”

Section: Introductionmentioning

confidence: 99%

Molecular Dynamics Simulations of Vacancy Generation and Migration near a Monocrystalline Silicon Surface during Energetic Cluster Ion Implantation

et al. 2020

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The process of ion implantation often involves vacancy generation and migration. The vacancy generation and migration near a monocrystalline silicon surface during three kinds of energetic Si35 cluster ion implantations were investigated by molecular dynamics simulations in the present work. The patterns of vacancy generation and migration, as well as the implantation-induced amorphous structure, were analyzed according to radial distribution function, Wigner–Seitz cell, and identify diamond structure analytical methods. A lot of vacancies rapidly generate and migrate in primary directions and form an amorphous structure in the first two picoseconds. The cluster with higher incident kinetic energy can induce the generation and migration of more vacancies and a deeper amorphous structure. Moreover, boundaries have a loading–unloading effect, where interstitial atoms load into the boundary, which then acts as a source, emitting interstitial atoms to the target and inducing the generation of vacancies again. These results provide more insight into doping silicon via ion implantation.

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Massive-scale molecular dynamics of ion-irradiated III–V compound semiconductors at the onset of nanopatterning

Cited by 2 publications

References 23 publications

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

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

Molecular Dynamics Simulations of Vacancy Generation and Migration near a Monocrystalline Silicon Surface during Energetic Cluster Ion Implantation

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