Numerical investigation of solute evaporation in crystal growth from solution: A case study of SiC growth by TSSG method

Dang, Yifan; Zhu, Chen; Liu, Xin; Yu, Wancheng; Liu, Xinbo; Suzuki, Koki; Furusho, Tomoaki; Harada, Shunta; Tagawa, Miho; Ujihara, Toru

doi:10.1016/j.jcrysgro.2021.126448

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…More details on the TSSG setup are found in the literature. [5,6,26] The CFD simulation was performed on a 2D steady-axisymmetric model by taking into account the heat transport, mass transport, and convection using CGSim software. [27] The temperatures inside the seed shaft (T), the rotation speeds of the seed shaft (𝜔 s ) and crucible (𝜔 c ), and the position of the crucible (z) were set as variable parameters.…”

Section: Cfd Simulationmentioning

confidence: 99%

Optimization of Flow Distribution by Topological Description and Machine Learning in Solution Growth of SiC

Isono

Harada

Kutsukake

et al. 2022

Advcd Theory and Sims

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The macroscopic distribution of fluid flows, which affect the quality of final products for various kinds of materials, is often difficult to describe in mathematical formulae and hinders the implementation of empirical knowledge in scaling up. In the present study, the characteristics of the flow distribution in silicon carbide (SiC) solution growth are described by using the position of the saddle point and the solution growth conditions are optimized by computational fluid dynamics simulation, machine learning, and a genetic algorithm. As a result, the candidates of the optimal condition for the solution growth of 6-in. SiC crystals are successfully obtained from the empirical knowledge gained from 3-in. crystal growth, by adding the topological description to the objective function. The present design of the objective function using the topological description can possibly be applied to other crystal growth or materials processing problems and to overcome scale-up difficulties, which can facilitate the rapid development of functional materials such as SiC wafers for power device applications. . IntroductionFluid flow that arises in many materials processing techniques, such as crystal growth, casting, thin film fabrication, and polymer processing, is critical to the quality and characteristics of the final product and optimization of the system. [1] Recent advances in computational fluid dynamics (CFD) aid in the design of suitable conditions for materials processing. [2] Furthermore, the

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Section: Cfd Simulationmentioning

confidence: 99%

Optimization of Flow Distribution by Topological Description and Machine Learning in Solution Growth of SiC

Isono

Harada

Kutsukake

et al. 2022

Advcd Theory and Sims

Self Cite

View full text Add to dashboard Cite

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“…Because of the high cost and extreme environment of real experiments, simulations have been used as a powerful alternative to understand the solution growth process for SiC. Previous numerical investigations can be divided into two groups depending on their focus: thermal, flow, and mass fields in the solution domain on the macroscale, − and step kinetics on the crystal surface on the microscale or mesoscale. − The former investigations provide exhaustive information about the environmental phase, but the scale is too large to reveal step-related phenomena on the growth front. The latter investigations, by contrast, depict the movement of elemental steps within a local area, but their scale is too small to be correlated with the practical growth conditions for a large-size crystal.…”

Section: Introductionmentioning

confidence: 99%

Modeling-Based Design of the Control Pattern for Uniform Macrostep Morphology in Solution Growth of SiC

Dang

Liu

Zhu

et al. 2023

Crystal Growth & Design

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In the solution growth of the SiC crystal, macrosteps with sufficient height on an off-axis substrate are required to reduce defects and achieve a high-quality grown layer. However, overdeveloped macrosteps can induce new defects and adversely affect the crystal quality. To better understand and control the behavior of macrosteps corresponding to the control parameters of the growth system, a simulation method that consists of a global twodimensional computational fluid dynamic (CFD) model, a local three-dimensional CFD model near the growth front, and a kinetics model that describes the movement of macrosteps on the crystal surface is proposed. The simulation method is first applied to investigate the effect of the crystal rotation speed on macrostep morphology. Although the results indicate that a higher crystal rotation speed results in less step bunching, constantly rotating the crystal in one direction is demonstrated to be incapable of yielding a uniform macrostep distribution on the whole surface. Accordingly, a sophisticated control pattern is designed by periodically switching the flow direction underneath the crystal surface, where the proposed simulation method is critical to determine detailed control-parameter values. When the control pattern suggested by the simulation is used, a grown crystal with a uniform macrostep morphology and ideal step height on the whole surface is obtained in the practical experiment.

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“…In addition, the operator must form a certain shape in which the crystal diameter is first reduced (called "necking") and then increase the diameter of the crystal to obtain a single crystal. Since the dynamics of the melt state depending on the input parameters are non-linear and complicated, it is difficult to simulate the FZ crystal growth process, as has been achieved for other crystal growth methods [29][30][31][32][33] . Thus, it is necessary to predict the dynamics of FZ crystal growth from the operation trajectories.…”

Section: Introductionmentioning

confidence: 99%

Data-driven automated control algorithm for floating-zone crystal growth derived by reinforcement learning

Harada

Tosa

Omae

et al. 2023

Preprint

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The complete automation of materials manufacturing with high productivity is a key problem in some materials processing. In floating zone (FZ) crystal growth, which is a manufacturing process for semiconductor wafers such as silicon, an operator adaptively controls the input parameters in accordance with the state of the crystal growth process. Since the operation dynamics of FZ crystal growth are complicated, automation is often difficult, and usually the process is manually controlled. Here we demonstrate automated control of FZ crystal growth by reinforcement learning using the dynamics predicted by Gaussian mixture modeling (GMM) from small numbers of trajectories. Our proposed method of constructing the control model is completely data-driven. Using an emulator program for FZ crystal growth, we show that the control model constructed by our proposed model can more accurately follow the ideal growth trajectory than demonstration trajectories created by human operation. Furthermore, we reveal that policy optimization near the demonstration trajectories realizes accurate control following the ideal trajectory.

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Numerical investigation of solute evaporation in crystal growth from solution: A case study of SiC growth by TSSG method

Cited by 10 publications

References 37 publications

Optimization of Flow Distribution by Topological Description and Machine Learning in Solution Growth of SiC

Optimization of Flow Distribution by Topological Description and Machine Learning in Solution Growth of SiC

Modeling-Based Design of the Control Pattern for Uniform Macrostep Morphology in Solution Growth of SiC

Data-driven automated control algorithm for floating-zone crystal growth derived by reinforcement learning

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