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Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks
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SYSNO ASEP 0541776 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks Tvůrce(i) Dropka, N. (DE)
Ecklebe, S. (DE)
Holeňa, Martin (UIVT-O) SAI, RIDČíslo článku 138 Zdroj.dok. Crystals. - : MDPI - ISSN 2073-4352
Roč. 11, č. 2 (2021)Poč.str. 13 s. Jazyk dok. eng - angličtina Země vyd. CH - Švýcarsko Klíč. slova neural networks ; crystal growth ; GaAs ; process control ; digital twins Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA18-18080S GA ČR - Grantová agentura ČR Způsob publikování Open access Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000622430500001 EID SCOPUS 85103909154 DOI 10.3390/cryst11020138 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2022 Elektronická adresa http://hdl.handle.net/11104/0319303
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