Fast Forecasting of VGF Crystal Growth Process by Dynamic Neural Networks

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Fast Forecasting of VGF Crystal Growth Process by Dynamic Neural Networks

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

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

Name of the periodical

Journal of Crystal Growth

ISSN

0022-0248

e-ISSN

—

Volume of the periodical

521

Issue of the periodical within the volume

1 September

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

6

Pages from-to

9-14

UT code for WoS article

000470831700002

EID of the result in the Scopus database

2-s2.0-85066255424