Smart Design of Cz-Ge Crystal Growth Furnace and Process

0567441 - ÚI 2023 RIV CH eng J - Journal Article
Dropka, N. - Tang, X. - Chappa, G. K. - Holeňa, Martin
Smart Design of Cz-Ge Crystal Growth Furnace and Process.
Crystals. Roč. 12, č. 12 (2022), č. článku 1764. ISSN 2073-4352. E-ISSN 2073-4352
Institutional support: RVO:67985807
Keywords : Czochralski Ge growth * CFD training data * furnace design * process design * regression tree * correlation coefficient
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 2.7, year: 2022
Method of publishing: Open access
https://dx.doi.org/10.3390/cryst12121764

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.
Permanent Link: https://hdl.handle.net/11104/0338696