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- ItemSmart Design of Cz-Ge Crystal Growth Furnace and Process(Basel : MDPI, 2022) Dropka, Natasha; Tang, Xia; Chappa, Gagan Kumar; Holena, MartinThe 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.
- ItemReal Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks(Basel : MDPI, 2021) Dropka, Natasha; Ecklebe, Stefan; Holena, MartinThe 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.
- ItemApplication of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials(Basel : MDPI, 2020) Dropka, Natasha; Holena, MartinIn 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.
- ItemNumerical Simulation of Species Segregation and 2D Distribution in the Floating Zone Silicon Crystals(Basel : MDPI, 2022) Surovovs, Kirils; Surovovs, Maksims; Sabanskis, Andrejs; Virbulis, Jānis; Dadzis, Kaspars; Menzel, Robert; Abrosimov, NikolayThe distribution of dopants and impurities in silicon grown with the floating zone method determines the electrical resistivity and other important properties of the crystals. A crucial process that defines the transport of these species is the segregation at the crystallization interface. To investigate the influence of the melt flow on the effective segregation coefficient as well as on the global species transport and the resulting distribution in the grown crystal, we developed a new coupled numerical model. Our simulation results include the shape of phase boundaries, melt flow velocity and temperature, species distribution in the melt and, finally, the radial and axial distributions in the grown crystal. We concluded that the effective segregation coefficient is not constant during the growth process but rather increases for larger melt diameters due to less intensive melt mixing.
- ItemArtificial Intelligence for Crystal Growth and Characterization(Basel : MDPI, 2022) Schimmel, Saskia; Sun, Wenhao; Dropka, Natasha[no abstract available]
- ItemIs Reduced Strontium Titanate a Semiconductor or a Metal?(Basel : MDPI, 2021) Rodenbücher, Christian; Guguschev, Christo; Korte, Carsten; Bette, Sebastian; Szot, KristofIn recent decades, the behavior of SrTiO3 upon annealing in reducing conditions has been under intense academic scrutiny. Classically, its conductivity can be described using point defect chemistry and predicting n-type or p-type semiconducting behavior depending on oxygen activity. In contrast, many examples of metallic behavior induced by thermal reduction have recently appeared in the literature, challenging this established understanding. In this study, we aim to resolve this contradiction by demonstrating that an initially insulating, as-received SrTiO3 single crystal can indeed be reduced to a metallic state, and is even stable against room temperature reoxidation. However, once the sample has been oxidized at a high temperature, subsequent reduction can no longer be used to induce metallic behavior, but semiconducting behavior in agreement with the predictions of point defect chemistry is observed. Our results indicate that the dislocation-rich surface layer plays a decisive role and that its local chemical composition can be changed depending on annealing conditions. This reveals that the prediction of the macroscopic electronic properties of SrTiO3 is a highly complex task, and not only the current temperature and oxygen activity but also the redox history play an important role.
- ItemLocal electronic structure in AlN studied by single-crystal 27Al and 14N NMR and DFT calculations(Basel : MDPI, 2020) Zeman, Otto E.O.; Moudrakovski, Igor L.; Hartmann, Carsten; Indris, Sylvio; Bräuniger, ThomasBoth the chemical shift and quadrupole coupling tensors for 14N and 27Al in the wurtzite structure of aluminum nitride have been determined to high precision by single-crystal NMR spectroscopy. A homoepitaxially grown AlN single crystal with known morphology was used, which allowed for optical alignment of the crystal on the goniometer axis. From the analysis of the rotation patterns of 14N (I = 1) and 27Al (I = 5/2), the quadrupolar coupling constants were determined to ?(14N) = (8.19 ± 0.02) kHz, and ?(27Al) = (1.914 ± 0.001) MHz. The chemical shift parameters obtained from the data fit were diso = -(292.6 ± 0.6) ppm and d? = -(1.9 ± 1.1) ppm for 14N, and (after correcting for the second-order quadrupolar shift) diso = (113.6 ± 0.3) ppm and d? = (12.7 ± 0.6) ppm for 27Al. DFT calculations of the NMR parameters for non-optimized crystal geometries of AlN generally did not match the experimental values, whereas optimized geometries came close for 27Al with ?calc = (1.791 ± 0.003) MHz, but not for 14N with ?calc = -(19.5 ± 3.3) kHz. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).