Modeling of industrial bulk crystal growth—state of the art and challenges

Fischer, B.; Friеdrich, J.; Jung, Thomas; Hainke, Marc; Dagner, Johannes; Fühner, Tim; Schwesig, P.

doi:10.1016/j.jcrysgro.2004.10.087

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…Simulation models mainly focused on predicting thermal field distribution of the system for equipment design. Such models were typically based on partial differential equations (PDE) describing the growth dynamics (Derby and Brown, 1986;Fischer et al, 2005). Müller (2002) proposed the concept of reverse simulation, which aimed at controlling a certain kind of defect given the defect-process relationships.…”

Section: State-of-the-artmentioning

confidence: 99%

Logistic regression for crystal growth process modeling through hierarchical nonnegative garrote-based variable selection

et al. 2016

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Single-crystal silicon ingots are produced from a complex crystal growth process. Such a process is sensitive to subtle process condition changes, which may easily become failed and lead to the growth of a polycrystalline ingot instead of the desired monocrystalline ingot. Therefore, it is important to model this polycrystalline defect in the crystal growth process and identify key process variables and their features. However, to model the crystal growth process poses great challenges due to complicated engineering mechanisms and a large amount of functional process variables. In this paper, we focus on modeling the relationship between a binary quality indicator for polycrystalline defect and functional process variables. We propose a logistic regression model with hierarchical nonnegative garrote based variable selection method, which can accurately estimate the model, identify key process variables, and capture important features. Simulations and a case study are conducted to illustrate the merits of the proposed method in prediction and variable selection.

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Section: State-of-the-artmentioning

confidence: 99%

Logistic regression for crystal growth process modeling through hierarchical nonnegative garrote-based variable selection

et al. 2016

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“…For example, dopant segregation and thermal stress on solidification are directly linked to the convective mass and heat transport in the melt, and their prediction and control is still one of the major challenges in crystal growth. Numerical modeling is ascribed a key role in predicting the melt flow and related phenomena [2].…”

Section: Introductionmentioning

confidence: 99%

A fixed-grid method for transient simulations of dopant segregation in VGF-RMF growth

Nikrityuk

Pätzold

Stelter

2012

Journal of Crystal Growth

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“…The use of global modeling is nowadays standard in the development of bulk growth processes [2]. Especially in the growth of compound semiconductors, the reduction of the dislocation density is one of the primary goals of process optimization.…”

Section: Introductionmentioning

confidence: 99%

Simulation of dislocation density: Global modeling of bulk crystal growth by a quasi-steady approach of the Alexander–Haasen concept

Bános

Friеdrich

Müller

2008

Journal of Crystal Growth

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Modeling of industrial bulk crystal growth—state of the art and challenges

Cited by 10 publications

References 9 publications

Logistic regression for crystal growth process modeling through hierarchical nonnegative garrote-based variable selection

Logistic regression for crystal growth process modeling through hierarchical nonnegative garrote-based variable selection

A fixed-grid method for transient simulations of dopant segregation in VGF-RMF growth

Simulation of dislocation density: Global modeling of bulk crystal growth by a quasi-steady approach of the Alexander–Haasen concept

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