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Smart Design of Cz-Ge Crystal Growth Furnace and Process
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SYSNO ASEP 0567441 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Smart Design of Cz-Ge Crystal Growth Furnace and Process Tvůrce(i) Dropka, N. (DE)
Tang, X. (DE)
Chappa, G. K. (DE)
Holeňa, Martin (UIVT-O) SAI, RIDČíslo článku 1764 Zdroj.dok. Crystals. - : MDPI - ISSN 2073-4352
Roč. 12, č. 12 (2022)Poč.str. 17 s. Forma vydání Online - E Jazyk dok. eng - angličtina Země vyd. CH - Švýcarsko Klíč. slova Czochralski Ge growth ; CFD training data ; furnace design ; process design ; regression tree ; correlation coefficient Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Způsob publikování Open access Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000900625400001 EID SCOPUS 85144718366 DOI 10.3390/cryst12121764 Anotace The aim of this study was to evaluate the potential of the machine learning technique of decision trees to understand the relationships among furnace design, process parameters, crystal quality, and yield in the case of the Czochralski growth of germanium. The ultimate goal was to provide the range of optimal values of 13 input parameters and the ranking of their importance in relation to their impact on three output parameters relevant to process economy and crystal quality. Training data were provided by CFD modelling. The variety of data was ensured by the Design of Experiments method. The results showed that the process parameters, particularly the pulling rate, had a substantially greater impact on the crystal quality and yield than the design parameters of the furnace hot zone. Of the latter, only the crucible size, the axial position of the side heater, and the material properties of the radiation shield were relevant. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2023 Elektronická adresa https://dx.doi.org/10.3390/cryst12121764
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