Fast forecasting of VGF crystal growth process by dynamic neural networks

Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

“…Still the studies are rare [18,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Only part of them were devoted to the crystal growth of semiconductors and oxides [18,[26][27][28][29][30][31][32][33]36,37]. Up to now, there have been two main research topics: optimization of the crystal growth process parameters and crystal growth process control by static and dynamic ANNs, respectively.…”

“…Beside a need for improved accuracy, for the practical application in process automation and control, it will be necessary to derive datasets from axisymmetric CFD simulations. Another concept for coping with process dynamics was proposed in a proof-of-concept study [28], where transient 1D CFD results of the simplified VGF-GaAs model provided the transient datasets of 2 heating powers and 5 temperatures at different axial positions in the melt and crystal and position of solid/liquid interface. Altogether 500 datasets were used for training a NARX type of dynamic ANN.…”

Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks

“…One feasible solution for the generation of digital twins and process control is the application of machine learning (ML) and particularly artificial neural networks (ANN) for the fast forecasting of the VGF-GaAs growth process. Application of ML to crystal growth is a new, but fast emerging and very promising field, as shown in, e.g., [3][4][5][6][7][8][9][10][11][12].…”

“…RNNs come in many variants [13]. In our previous proof-of-concept study [3], we used the Nonlinear AutoRegressive eXogenous (NARX) [14] type of RNNs for the prediction of temperature profiles and the s/l interface position in VGF-GaAs growth. The predictions were accurate for slow growth rates, but their accuracy significantly decreased with the increase in the crystal growth rate.…”

Toward Precise n-Type Doping Control in MOVPE-Grown β-Ga2O3 Thin Films by Deep-Learning Approach

“…In the field of MOVPE-grown β-Ga 2 O 3 , the observed doping level is usually limited to two situations: (1) only the relation between the doping level and a single process parameter is revealed, e.g., the chamber pressure [13,14] and the concentration of the Si precursor [12,15], and (2) the reported results are usually collected in different deposition systems, which are difficult to be applied to other systems directly. To deal with the above limitations, datadriven approaches such as machine learning and deep learning are seen as promising tools that are applied to explore the ample process-parameter space and understand the nonlinear relationship between them [16], and have already demonstrated a wide application in different research topics such as bulk crystal growth [17][18][19][20], thin-film growth [21][22][23], molecular-property prediction [24][25][26], and chemical-reaction development [27]. Nevertheless, the available dataset in lab-level research is usually small due to the limited workforce and faculty resources, which is a critical issue for a data-driven methodology such as deep learning.…”