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Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques
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SYSNO ASEP 0547633 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques Author(s) Dropka, N. (DE)
Böttcher, K. (DE)
Holeňa, Martin (UIVT-O) SAI, RIDNumber of authors 3 Article number 1218 Source Title Crystals. - : MDPI - ISSN 2073-4352
Roč. 11, č. 10 (2021)Number of pages 20 s. Language eng - English Country CH - Switzerland Keywords VGF-GaAs growth ; machine learning ; data mining ; decision trees ; correlation analysis ; PCA biplot ; k-means clustering 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 000717001300001 EID SCOPUS 85117286796 DOI 10.3390/cryst11101218 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2022 Electronic address http://dx.doi.org/10.3390/cryst11101218
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