Fast Forecasting of VGF Crystal Growth Process by Dynamic Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00505764" target="_blank" >RIV/67985807:_____/19:00505764 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.jcrysgro.2019.05.022" target="_blank" >http://dx.doi.org/10.1016/j.jcrysgro.2019.05.022</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jcrysgro.2019.05.022" target="_blank" >10.1016/j.jcrysgro.2019.05.022</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast Forecasting of VGF Crystal Growth Process by Dynamic Neural Networks

Popis výsledku v původním jazyce

Fast forecasting of process variables during the crystal growth is a critical step in a process development, optimization and control. The common approach based on computational fluid dynamics modeling is accurate, but too slow to deliver results in real time. Here we conducted a feasibility study on the application of dynamic artificial neural networks in the forecasting of VGF-GaAs crystal growth cooling program. Particularly, we studied various Nonlinear-AutoRegressive artificial neural networks with eXogenous inputs (NARX) with 2 external inputs and 6 outputs derived from 500 transient data sets. Data were generated by transient 1D CFD simulation. The first encouraging results are presented and the pros and cons of the application of dynamic artificial neural networks for the fast predictions of VGF process parameters are discussed.

Název v anglickém jazyce

Fast Forecasting of VGF Crystal Growth Process by Dynamic Neural Networks

Popis výsledku anglicky

Fast forecasting of process variables during the crystal growth is a critical step in a process development, optimization and control. The common approach based on computational fluid dynamics modeling is accurate, but too slow to deliver results in real time. Here we conducted a feasibility study on the application of dynamic artificial neural networks in the forecasting of VGF-GaAs crystal growth cooling program. Particularly, we studied various Nonlinear-AutoRegressive artificial neural networks with eXogenous inputs (NARX) with 2 external inputs and 6 outputs derived from 500 transient data sets. Data were generated by transient 1D CFD simulation. The first encouraging results are presented and the pros and cons of the application of dynamic artificial neural networks for the fast predictions of VGF process parameters are 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/GA18-18080S" target="_blank" >GA18-18080S: Objevování znalostí v datech o aktivitě člověka založené na fúzi</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

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

521

Číslo periodika v rámci svazku

1 September

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

9-14

Kód UT WoS článku

000470831700002

EID výsledku v databázi Scopus

2-s2.0-85066255424