Fast Forecasting of VGF Crystal Growth Process by Dynamic Neural Networks

0505764 - ÚI 2020 RIV NL eng J - Journal Article
Dropka, N. - Holeňa, Martin - Ecklebe, S. - Frank-Rotsch, C. - Winkler, J.
Fast Forecasting of VGF Crystal Growth Process by Dynamic Neural Networks.
Journal of Crystal Growth. Roč. 521, 1 September (2019), s. 9-14. ISSN 0022-0248. E-ISSN 1873-5002
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : Computer simulation * Fluid flows * Gradient freeze technique
OECD category: Condensed matter physics (including formerly solid state physics, supercond.)
Impact factor: 1.632, year: 2019
Method of publishing: Limited access
http://dx.doi.org/10.1016/j.jcrysgro.2019.05.022

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.
Permanent Link: http://hdl.handle.net/11104/0297153