Optimization of Magnetically Driven Directional Solidification of Silicon Using Artificial Neural Networks and Gaussian Process Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00476581" target="_blank" >RIV/67985807:_____/17:00476581 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.jcrysgro.2017.05.007" target="_blank" >http://dx.doi.org/10.1016/j.jcrysgro.2017.05.007</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jcrysgro.2017.05.007" target="_blank" >10.1016/j.jcrysgro.2017.05.007</a>

Result language
angličtina
Original language name
Optimization of Magnetically Driven Directional Solidification of Silicon Using Artificial Neural Networks and Gaussian Process Models
Original language description
In directional solidification of silicon, the solid-liquid interface shape plays a crucial role for the quality of crystals. The interface shape can be influenced by forced convection using travelling magnetic fields. Up to now, there is no general and explicit methodology to identify the relation and the optimum combination of magnetic and growth parameters e.g., frequency, phase shift, current magnitude and interface deflection in a buoyancy regime. In the present study, 2D CFD modeling was used to generate data for the design and training of artificial neural networks and for Gaussian process modeling. The aim was to quickly assess the complex nonlinear dependences among the parameters and to optimize them for the interface flattening. The first encouraging results are presented and the pros and cons of artificial neural networks and Gaussian process modeling discussed.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10302 - Condensed matter physics (including formerly solid state physics, supercond.)

Project
<a href="/en/project/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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