Počet záznamů: 1
Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques
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SYSNO ASEP 0547633 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques Tvůrce(i) Dropka, N. (DE)
Böttcher, K. (DE)
Holeňa, Martin (UIVT-O) SAI, RIDCelkový počet autorů 3 Číslo článku 1218 Zdroj.dok. Crystals. - : MDPI - ISSN 2073-4352
Roč. 11, č. 10 (2021)Poč.str. 20 s. Jazyk dok. eng - angličtina Země vyd. CH - Švýcarsko Klíč. slova VGF-GaAs growth ; machine learning ; data mining ; decision trees ; correlation analysis ; PCA biplot ; k-means clustering 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 000717001300001 EID SCOPUS 85117286796 DOI 10.3390/cryst11101218 Anotace The aim of this study was to assess the ability of the various data mining and supervised machine learning techniques: correlation analysis, k-means clustering, principal component analysis and decision trees (regression and classification), to derive, optimize and understand the factors influencing VGF-GaAs growth. Training data were generated by Computational Fluid Dynamics (CFD) simulations and consisted of 130 datasets with 6 inputs (growth rate and power of 5 heaters) and 5 outputs (interface position and deflection, and temperatures at various positions in GaAs). Data mining results confirmed a good dispersion of the training data without the feasibility of a dimensionality reduction. Data clustering was observed in relation to the position of the crystallization front relative to the side heaters. Based on the statistical performance criteria and training results, decision trees identified the most decisive inputs and their ranges for a favorable interface shape and to keep GaAs temperature beyond limits for heavy arsenic evaporation. Decision trees are a recommendable machine learning technique with short training times and acceptable predictive accuracy based on small volume of CFD training data, capable of providing guidelines for understanding the crystal growth process, which is a prerequisite for the growth of low-cost, high-quality bulk crystals. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2022 Elektronická adresa http://dx.doi.org/10.3390/cryst11101218
Počet záznamů: 1