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20 pages, 3149 KB

Open AccessArticle

Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques

by Natasha Dropka, Klaus Böttcher and Martin Holena

Crystals 2021, 11(10), 1218; https://doi.org/10.3390/cryst11101218 - 9 Oct 2021

Cited by 17 | Viewed by 4137

The aim of this study was to assess the ability of the various data mining and supervised machine learning techniques: correlation analysis, k-means clustering, principal component analysis and decision trees (regression and classification), to derive, optimize and understand the factors influencing VGF-GaAs growth. Training data were generated by Computational Fluid Dynamics (CFD) simulations and consisted of 130 datasets with 6 inputs (growth rate and power of 5 heaters) and 5 outputs (interface position and deflection, and temperatures at various positions in GaAs). Data mining results confirmed a good dispersion of the training data without the feasibility of a dimensionality reduction. Data clustering was observed in relation to the position of the crystallization front relative to the side heaters. Based on the statistical performance criteria and training results, decision trees identified the most decisive inputs and their ranges for a favorable interface shape and to keep GaAs temperature beyond limits for heavy arsenic evaporation. Decision trees are a recommendable machine learning technique with short training times and acceptable predictive accuracy based on small volume of CFD training data, capable of providing guidelines for understanding the crystal growth process, which is a prerequisite for the growth of low-cost, high-quality bulk crystals. Full article

(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)

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13 pages, 3394 KB

Open AccessArticle

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

by Natasha Dropka, Stefan Ecklebe and Martin Holena

Crystals 2021, 11(2), 138; https://doi.org/10.3390/cryst11020138 - 29 Jan 2021

Cited by 7 | Viewed by 2941

Abstract

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. Full article

(This article belongs to the Special Issue Artificial Intelligence for Crystal Growth and Characterization)

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