Application of machine learning in single crystal growth

0540767 - FZÚ 2021 RIV SK eng C - Konferenční příspěvek (zahraniční konf.)
Hájek, František - Dominec, Filip - Hospodková, Alice - Oswald, Jiří
Application of machine learning in single crystal growth.
20th Conference of Czech and Slovak Physicists Proceedings. Košice: Slovak Physical Society, Czech Physical Society, 2020 - (Džubinská, A.; Reiffers, M.), s. 87-88. ISBN 978-80-89855-13-1.
[Conference of Czech and Slovak Physicists /20./. Prague (CZ), 07.09.2020-10.09.2020]
Grant CEP: GA MŠMT(CZ) LO1603
Institucionální podpora: RVO:68378271
Klíčová slova: crystal growth * semiconductors * machine learning
Obor OECD: Condensed matter physics (including formerly solid state physics, supercond.)

Development of a new material with required properties is a very complex task. Theoretical models for growth procedure are usually not available, at least at the beginning. Therefore, many attempts are made to achieve required properties of the material and many characterization datasets are obtained. However, the way how the physical properties of the material are affected by the growth conditions does not have to be straightforwardly evident. For such a case, machine learning can be very helpful. In this contribution, applications of several simple machine learning approaches are applied to the development process of the InGaN/GaN scintillator structure. A properly trained neural network is capable to predict luminescence properties from the growth parameters of the structure. This enables optimization of the growth parameters from empirical data only. On the other hand, understanding of underlying physics is not guaranteed but the predictions of the model can give a clue.
Trvalý link: http://hdl.handle.net/11104/0318367