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

Dropka, Natasha; Ecklebe, Stefan; Holeňa, Martin

doi:10.3390/cryst11020138

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…applied long short‐term memory neural network and successfully predicted the process dynamics in a simplified one dimensional growth of gallium arsenide crystal. [ 11 ] However, 1950 datasets, each including 100 time steps, were utilized to train the model, and this data amount is expected to become larger and unaffordable for a real system with three‐dimensional features and more parameters. The present authors previously proposed an adaptive control method based on machine learning for designing a time‐dependent control recipe that can adapt to geometric evolution.…”

Section: Introductionmentioning

confidence: 99%

A Transfer Learning‐Based Method for Facilitating the Prediction of Unsteady Crystal Growth

Dang

Kutsukake

Liu

et al. 2022

Advcd Theory and Sims

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Real-time prediction and dynamic control systems that can adapt to an unsteady environment are necessary for material fabrication processes, especially crystal growth. Recent studies have demonstrated the effectiveness of machine learning in predicting an unsteady crystal growth process, but its wider application is hindered by the large amount of training data required for sufficient accuracy. To address this problem, this study investigates the capability of transfer learning to predict geometric evolution in an unsteady silicon carbide (SiC) solution growth system based on a small amount of data. The performance of transferred models is discussed regarding the effect of the transfer learning method, training data amount, and time step length. The transfer learning strategy yields the same accuracy as that of training from scratch but requires only 20% of the training data. The accuracy is stably inherited through successive time steps, which demonstrates the effectiveness of transfer learning in reducing the required amount of training data for predicting evolution in an unsteady crystal growth process. Moreover, the transferred models trained with relatively more data (no more than 100%) further improve the accuracy inherited from the source model through multiple time steps, which broadens the application scope of transfer learning.

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Section: Introductionmentioning

confidence: 99%

A Transfer Learning‐Based Method for Facilitating the Prediction of Unsteady Crystal Growth

Dang

Kutsukake

Liu

et al. 2022

Advcd Theory and Sims

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“…The recent tremendous success of artificial neural networks (ANN) [3] in detecting the complex patterns and relationships in non-linear static and dynamic data sets in related fields (e.g., [4]) has triggered feasibility studies on the application of ANN for the prediction of transport phenomena in crystal growth furnaces of semiconductors and optimization of growth parameters, inter alia [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In this case, the number and specification of the independent and optimization parameters are constrained only by the availability of the training data and not by the method.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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“…The application of AI methods to subdisciplines with smaller numbers of data, such as bulk crystal growth, is only nascent. Nevertheless, four articles in this Special Issue have addressed the growth of semiconductor materials as bulk crystals or thin films [4][5][6][7]. Two of them target the growth of GaAs via the Vertical-Gradient-Freeze (VGF) method [4,5].…”

mentioning

confidence: 99%

“…Nevertheless, four articles in this Special Issue have addressed the growth of semiconductor materials as bulk crystals or thin films [4][5][6][7]. Two of them target the growth of GaAs via the Vertical-Gradient-Freeze (VGF) method [4,5]. The successful application of recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics, including temperatures and the position of the solid-liquid interface, was realized using a dataset of transient numerical simulations [4].…”

mentioning

confidence: 99%

“…Two of them target the growth of GaAs via the Vertical-Gradient-Freeze (VGF) method [4,5]. The successful application of recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics, including temperatures and the position of the solid-liquid interface, was realized using a dataset of transient numerical simulations [4]. The other contribution on VGF growth of GaAs evaluates the capabilities of various datamining and supervised machine-learning techniques for identifying and optimizing the key factors that influence the growth process [5].…”

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confidence: 99%

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