The Prediction Model of Crystal Growth Simulation Built by Machine Learning and Its Applications

Ujihara, Toru; Tsunooka, Yosuke; Hatasa, Goki; Kutsukake, Kentaro; Ishiguro, Akio; Murayama, Keiji; Narumi, Taka; Harada, Shunta; Tagawa, Miho

doi:10.1380/vss.62.136

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…After this improvement, fast calculation comparable to process time is the next issue as the second factor. Currently, machine learning, 100 – 104 fusion physical model with machine learning, 105 , 106 and surrogate models 107 , 108 are adopted to solve this issue. Figure 48 shows an example of a fusion model (or hybrid model) in which incident gas fluxes derived from machine learning using real-time monitoring of plasma and EES were used as an input and feature scale profiles, and damage distributions were simulated by a physical model.…”

Section: Future Perspectivesmentioning

confidence: 99%

Review and future perspective of feature scale profile modeling for high-performance semiconductor devices

Kuboi

2023

J. Micro/Nanopattern. Mats. Metro.

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.Modeling and simulation of feature scale profiles, including damage distributions and film properties in dry etching (continuous wave, pulse, cycle, and atomic layer etching) and deposition processes (chemical vapor, physical vapor, and atomic layer deposition) using string, shock tracking, level-set, and cell removable (or voxel) methods, are useful for predicting process properties and gain insights into the variation mechanisms for realizing high performance of advanced complementary metal-oxide semiconductor devices and have been comprehensively reviewed in this work. The future perspective has also been discussed from the viewpoint of a fusion physical model with machine learning using real-time monitoring of the manufacturing equipment, so-called process informatics. Furthermore, after the introduction of quantum computing for process simulations on atomic scales, the epoch of full digital twins might be realized.

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Section: Future Perspectivesmentioning

confidence: 99%

Review and future perspective of feature scale profile modeling for high-performance semiconductor devices

Kuboi

2023

J. Micro/Nanopattern. Mats. Metro.

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“…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.…”

Section: Ai Applications In Crystal Growth: State Of the Artmentioning

confidence: 99%

“…The superposition of the GA to the ANN prediction model enabled more optimum conditions to be found. The prediction of the growth conditions for upscaled SiC crystals using the same methodology was the topic of the authors' further papers [32,33].…”

Section: Static Ann Applicationsmentioning

confidence: 99%

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

Dropka

Holeňa

2020

Crystals

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In 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.

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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 Prediction Model of Crystal Growth Simulation Built by Machine Learning and Its Applications

Cited by 7 publications

References 10 publications

Review and future perspective of feature scale profile modeling for high-performance semiconductor devices

Review and future perspective of feature scale profile modeling for high-performance semiconductor devices

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

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

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