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  1. 1.
    0585427 - ÚI 2025 DE eng J - Journal Article
    Dropka, N. - Böttcher, K. - Chappa, G. K. - Holeňa, Martin
    Data-Driven Cz–Si Scale-Up under Conditions of Partial Similarity.
    Crystal Research and Technology. Online 09 April 2024 (2024). ISSN 0232-1300. E-ISSN 1521-4079
    Institutional support: RVO:67985807
    Keywords : artificial neural networks * Cz–Si growth * data-driven scale up * partial similarity * Voronkov criteria
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 1.5, year: 2022
    Method of publishing: Open access
    https://doi.org/10.1002/crat.202300342
    Permanent Link: https://hdl.handle.net/11104/0353135
    FileDownloadSizeCommentaryVersionAccess
    0585427-oaonl.pdf05.5 MBOA CC BY 4.0Publisher’s postprintopen-access
     
     
  2. 2.
    0579923 - ÚI 2024 RIV CH eng J - Journal Article
    Tang, X. - Chappa, G. K. - Viera, L. - Holeňa, Martin - Dropka, N.
    Decision Tree-Supported Analysis of Gallium Arsenide Growth Using the LEC Method.
    Crystals. Roč. 13, č. 12 (2023), s. 1659. ISSN 2073-4352. E-ISSN 2073-4352
    Institutional support: RVO:67985807
    Keywords : LEC growth * gallium arsenide * CFD * regression tree
    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://doi.org/10.3390/cryst13121659
    Permanent Link: https://hdl.handle.net/11104/0348712
    FileDownloadSizeCommentaryVersionAccess
    0579923-aoa.pdf25.7 MBOA CC BY 4.0Author´s preprintopen-access
     
     
  3. 3.
    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
    Permanent Link: https://hdl.handle.net/11104/0338696
     
     
  4. 4.
    0547633 - ÚI 2022 RIV CH eng J - Journal Article
    Dropka, N. - Böttcher, K. - Holeňa, Martin
    Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques.
    Crystals. Roč. 11, č. 10 (2021), č. článku 1218. ISSN 2073-4352. E-ISSN 2073-4352
    R&D Projects: GA ČR(CZ) GA18-18080S
    Institutional support: RVO:67985807
    Keywords : VGF-GaAs growth * machine learning * data mining * decision trees * correlation analysis * PCA biplot * k-means clustering
    OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
    Impact factor: 2.670, year: 2021
    Method of publishing: Open access
    http://dx.doi.org/10.3390/cryst11101218
    Permanent Link: http://hdl.handle.net/11104/0323829
    FileDownloadSizeCommentaryVersionAccess
    0547633-afin.pdf33.2 MBOA CC BY 4.0Publisher’s postprintopen-access
     
     


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