Smart Design of Cz-Ge Crystal Growth Furnace and Process
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Crystals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4352/12/12/1764 |
| _version_ | 1797460763830386688 |
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| author | Natasha Dropka Xia Tang Gagan Kumar Chappa Martin Holena |
| author_facet | Natasha Dropka Xia Tang Gagan Kumar Chappa Martin Holena |
| author_sort | Natasha Dropka |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T17:09:41Z |
| format | Article |
| id | doaj.art-b8df9107bdce4376bc618f5ebd39f774 |
| institution | Directory Open Access Journal |
| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-03-09T17:09:41Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-b8df9107bdce4376bc618f5ebd39f7742023-11-24T14:10:33ZengMDPI AGCrystals2073-43522022-12-011212176410.3390/cryst12121764Smart Design of Cz-Ge Crystal Growth Furnace and ProcessNatasha Dropka0Xia Tang1Gagan Kumar Chappa2Martin Holena3Leibniz-Institut für Kristallzüchtung, Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz-Institut für Kristallzüchtung, Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz-Institut für Kristallzüchtung, Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz Institute for Catalysis, Albert-Einstein-Str. 29A, 18069 Rostock, GermanyThe 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.https://www.mdpi.com/2073-4352/12/12/1764Czochralski Ge growthCFD training datafurnace designprocess designregression treecorrelation coefficient |
| spellingShingle | Natasha Dropka Xia Tang Gagan Kumar Chappa Martin Holena Smart Design of Cz-Ge Crystal Growth Furnace and Process Crystals Czochralski Ge growth CFD training data furnace design process design regression tree correlation coefficient |
| title | Smart Design of Cz-Ge Crystal Growth Furnace and Process |
| title_full | Smart Design of Cz-Ge Crystal Growth Furnace and Process |
| title_fullStr | Smart Design of Cz-Ge Crystal Growth Furnace and Process |
| title_full_unstemmed | Smart Design of Cz-Ge Crystal Growth Furnace and Process |
| title_short | Smart Design of Cz-Ge Crystal Growth Furnace and Process |
| title_sort | smart design of cz ge crystal growth furnace and process |
| topic | Czochralski Ge growth CFD training data furnace design process design regression tree correlation coefficient |
| url | https://www.mdpi.com/2073-4352/12/12/1764 |
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