Abstract
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.
Highlights
The development and optimization of bulk crystal growth processes is a demanding task due to the multidisciplinarity of the phenomena associated with a phase change, numerous process parameters, challenging scale up and, in particular, the dynamic nature of the process with a considerable time delay [1].Conventional experimental and Computational Fluid Dynamics (CFD) approaches to derive crystal growth process recipes are laborious, costly and time consuming
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
Principal component analysis (PCA) is a data mining technique typically used to structure, simplify and illustrate large data sets by approximating a large number of statistical variables with a smaller number of linear combinations that retain as large part of the overall variance as possible
Summary
The development and optimization of bulk crystal growth processes is a demanding task due to the multidisciplinarity of the phenomena associated with a phase change, numerous process parameters, challenging scale up and, in particular, the dynamic nature of the process with a considerable time delay [1]. 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. It is important to note that the applied methodology can be adopted by other materials and growth processes
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