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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10302 - Condensed matter physics (including formerly solid state physics, supercond.)

Projekt

<a href="/cs/project/GA17-01251S" target="_blank" >GA17-01251S: Metaučení pro extrakci pravidel s numerickými konsekventy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Crystal Growth

ISSN

0022-0248

e-ISSN

—

Svazek periodika

471

Číslo periodika v rámci svazku

1 August

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

53-61

Kód UT WoS článku

000403624100008

EID výsledku v databázi Scopus

2-s2.0-85019609208