Optimization of the controlling recipe in quasi-single crystalline silicon growth using artificial neural network and genetic algorithm

Dang, Yifan; Liu, Lijun; Li, Zaoyang

doi:10.1016/j.jcrysgro.2019.06.033

Cited by 27 publications

(17 citation statements)

References 18 publications

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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%

“…Concerning static applications, in the papers [25,26,29,31], feed-forward networks of either the mono-or multi-layer perceptron type were used to model dependences pertaining to crystal growth process.…”

Section: Static Ann Applicationsmentioning

confidence: 99%

“…More researchers studied the application of static ANN combined with GA [18,31] or the Adam optimization method [39] for the optimization of parameters affecting the crystal growth [26].…”

Section: Static Ann Applicationsmentioning

confidence: 99%

“…The crystal growth process dynamics described by static feed-forward ANN was the topic of a paper [31]. In this study, 54 transient axisymmetric 2D CFD simulations were used to derive the cooling rates of two heaters and the velocity of the heat gate during directional solidification of 850 kg quasimono Si crystals in industrial G6 size furnace.…”

Section: Dynamic Applicationsmentioning

confidence: 99%

“…Optimization of controlling recipe in quasi-mono Si growth using feed-forward ANN coupled with GA: (a) Configuration of a G6-size industrial seeded directional solidification (DS) furnace, (b) ANN architecture, (c) thermal stress field in the grown crystals between the original controlling recipe (left) and the optimal recipe (right), (d) original and the optimal growth recipe. Reprinted from[31] with permission from Elsevier.…”

mentioning

confidence: 99%

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

confidence: 99%

Section: Static Ann Applicationsmentioning

confidence: 99%

“…More researchers studied the application of static ANN combined with GA [18,31] or the Adam optimization method [39] for the optimization of parameters affecting the crystal growth [26].…”

Section: Static Ann Applicationsmentioning

confidence: 99%

Section: Dynamic Applicationsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Dropka

Holeňa

2020

Crystals

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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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Exploring mc‐Silicon Wafers: Utilizing Machine Learning to Enhance Wafer Quality Through Etching Studies

Raji,

Balakrishnapillai Suseela,

Manikkam

et al. 2024

Cryst. Res. Technol.

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This paper provides a method for improving the photovoltaic conversion efficiency and optical attributes of silicon solar cells manufactured from as‐cut boron doped p‐type multi‐crystalline silicon wafers using acid‐based chemical texturization via machine learning. A decreased reflectance, which can be attained by the right chemical etching conditions, is one of the key elements for raising solar cell efficiency. In this work, the mc‐Silicon wafer surface reflectance is obtained under (<2%) after optimization of wet chemical etching. The HF + HNO3 + CH3COOH chemical etchant is used in the ratio 1:3:2 at different conditions of the etching duration of 1 min, 2 min, 3 min, and 4 min, respectively. The as‐cut boron doped p‐type mc‐silicon wafers are analysed with ultraviolet–visible spectroscopy, optical microscopy, Fourier transforms infrared spectroscopy, thickness profilometer, and scanning electron microscopy before and after etching. The chemical etching solution produces good results in 3 min etched wafer, with a reflectivity value of <2%.The reflectivity and optical images are inputs to the convolutional neural network model and the linear regression model to obtain the etching rate for better reflectivity. The classification model provides 99.6% accuracy and the regression model results in the minimum mean squared error (MSE) of 0.062.

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Optimization of the controlling recipe in quasi-single crystalline silicon growth using artificial neural network and genetic algorithm

Cited by 27 publications

References 18 publications

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

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

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

Exploring mc‐Silicon Wafers: Utilizing Machine Learning to Enhance Wafer Quality Through Etching Studies

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