Search results
- 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/11104/0353135File Download Size Commentary Version Access 0585427-oaonl.pdf 0 5.5 MB OA CC BY 4.0 Publisher’s postprint open-access - 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/11104/0348712File Download Size Commentary Version Access 0579923-aoa.pdf 2 5.7 MB OA CC BY 4.0 Author´s preprint open-access - 3.0576081 - ÚI 2025 DE eng J - Journal Article
Dropka, N. - Holeňa, Martin - Thieme, C. - Chou, T.-S.
Development of the VGF Crystal Growth Recipe: Intelligent Solutions of Ill-Posed Inverse Problems using Images and Numerical Data.
Crystal Research and Technology. Online first 23 August 2023 (2024). ISSN 0232-1300. E-ISSN 1521-4079
Institutional support: RVO:67985807
Keywords : artificial neural networks * image data * inverse problems * Kriging * numerical data * reduced order modelling * VGF growth
Impact factor: 1.5, year: 2022
Method of publishing: Open access
https://dx.doi.org/10.1002/crat.202300125
Permanent Link: https://hdl.handle.net/11104/0345705File Download Size Commentary Version Access 0576081-aonloa.pdf 0 1.8 MB OA CC BY 4.0 Publisher’s postprint open-access - 4.0567441 - 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 - 5.0547633 - 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/11104/0323829File Download Size Commentary Version Access 0547633-afin.pdf 3 3.2 MB OA CC BY 4.0 Publisher’s postprint open-access - 6.0541776 - ÚI 2022 RIV CH eng J - Journal Article
Dropka, N. - Ecklebe, S. - Holeňa, Martin
Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks.
Crystals. Roč. 11, č. 2 (2021), č. článku 138. ISSN 2073-4352. E-ISSN 2073-4352
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : neural networks * crystal growth * GaAs * process control * digital twins
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
Permanent Link: http://hdl.handle.net/11104/0319303File Download Size Commentary Version Access 541776-aoa.pdf 2 3.5 MB OA CC BY 4.0 Publisher’s postprint open-access - 7.0505764 - ÚI 2020 RIV NL eng J - Journal Article
Dropka, N. - Holeňa, Martin - Ecklebe, S. - Frank-Rotsch, C. - Winkler, J.
Fast Forecasting of VGF Crystal Growth Process by Dynamic Neural Networks.
Journal of Crystal Growth. Roč. 521, 1 September (2019), s. 9-14. ISSN 0022-0248. E-ISSN 1873-5002
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : Computer simulation * Fluid flows * Gradient freeze technique
OECD category: Condensed matter physics (including formerly solid state physics, supercond.)
Impact factor: 1.632, year: 2019
Method of publishing: Limited access
http://dx.doi.org/10.1016/j.jcrysgro.2019.05.022
Permanent Link: http://hdl.handle.net/11104/0297153 - 8.0476581 - ÚI 2018 RIV NL eng J - Journal Article
Dropka, N. - Holeňa, Martin
Optimization of Magnetically Driven Directional Solidification of Silicon Using Artificial Neural Networks and Gaussian Process Models.
Journal of Crystal Growth. Roč. 471, 1 August (2017), s. 53-61. ISSN 0022-0248. E-ISSN 1873-5002
R&D Projects: GA ČR GA17-01251S
Institutional support: RVO:67985807
Keywords : computer simulation * fluid flows * magnetic fields * directional solidification * semiconducting silicon
OECD category: Condensed matter physics (including formerly solid state physics, supercond.)
Impact factor: 1.742, year: 2017
Permanent Link: http://hdl.handle.net/11104/0273056