Filters

2020 - 20212

More filters

2021 - 20222
Reset filters

Settings

DDC: 540 × Type: Text × Version: publishedVersion × Publisher: Basel : MDPI × Subject: Crystal growth × WGL Institute: LIKAT ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks
    (Basel : MDPI, 2021) Dropka, Natasha; Ecklebe, Stefan; Holena, Martin
    The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations. Real time predictions of the temperatures and solid–liquid interface position in GaAs are crucial for control applications and for process visualization, i.e., for generation of digital twins. In the reported study, an LSTM network was trained on 1950 datasets with 2 external inputs and 6 outputs. Based on network performance criteria and training results, LSTMs showed the very accurate predictions of the VGF-GaAs growth process with median root-mean-square-error (RMSE) values of 2 × 10−3. This deep learning method achieved a superior predictive accuracy and timeliness compared with more traditional Nonlinear AutoRegressive eXogenous (NARX) recurrent networks.
  • Thumbnail Image
    Item
    Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials
    (Basel : MDPI, 2020) Dropka, Natasha; Holena, Martin
    In this review, we summarize the results concerning the application of artificial neural networks (ANNs) in the crystal growth of electronic and opto-electronic materials. The main reason for using ANNs is to detect the patterns and relationships in non-linear static and dynamic data sets which are common in crystal growth processes, all in a real time. The fast forecasting is particularly important for the process control, since common numerical simulations are slow and in situ measurements of key process parameters are not feasible. This important machine learning approach thus makes it possible to determine optimized parameters for high-quality up-scaled crystals in real time. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.