Number of the records: 1
Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks
- 1.
SYSNO ASEP 0541776 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks Author(s) Dropka, N. (DE)
Ecklebe, S. (DE)
Holeňa, Martin (UIVT-O) SAI, RIDArticle number 138 Source Title Crystals. - : MDPI - ISSN 2073-4352
Roč. 11, č. 2 (2021)Number of pages 13 s. Language eng - English Country CH - Switzerland Keywords neural networks ; crystal growth ; GaAs ; process control ; digital twins Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA18-18080S GA ČR - Czech Science Foundation (CSF) Method of publishing Open access Institutional support UIVT-O - RVO:67985807 UT WOS 000622430500001 EID SCOPUS 85103909154 DOI 10.3390/cryst11020138 Annotation The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations. Real time predictions of the temperatures and solid–liquid interface position in GaAs are crucial for control applications and for process visualization, i.e., for generation of digital twins. In the reported study, an LSTM network was trained on 1950 datasets with 2 external inputs and 6 outputs. Based on network performance criteria and training results, LSTMs showed the very accurate predictions of the VGF-GaAs growth process with median root-mean-square-error (RMSE) values of 2 × 10−3. This deep learning method achieved a superior predictive accuracy and timeliness compared with more traditional Nonlinear AutoRegressive eXogenous (NARX) recurrent networks. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2022 Electronic address http://hdl.handle.net/11104/0319303
Number of the records: 1