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

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...

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Main Authors:	Natasha Dropka, Martin Holena
Format:	Article
Language:	English
Published:	MDPI AG 2020-08-01
Series:	Crystals
Subjects:	artificial neural networks crystal growth semiconductors oxides
Online Access:	https://www.mdpi.com/2073-4352/10/8/663

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spelling	doaj-40f875a7dc5647418d879ed30dcafd1d2020-11-25T03:10:21ZengMDPI AGCrystals2073-43522020-08-011066366310.3390/cryst10080663Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic MaterialsNatasha Dropka0Martin Holena1Leibniz-Institut für Kristallzüchtung, Max-Born-Str. 2, 12489 Berlin, GermanyLeibniz Institute for Catalysis, Albert-Einstein-Str. 29A, 18069 Rostock, GermanyIn 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.https://www.mdpi.com/2073-4352/10/8/663artificial neural networkscrystal growthsemiconductorsoxides
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Natasha Dropka Martin Holena
spellingShingle	Natasha Dropka Martin Holena Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials Crystals artificial neural networks crystal growth semiconductors oxides
author_facet	Natasha Dropka Martin Holena
author_sort	Natasha Dropka
title	Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials
title_short	Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials
title_full	Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials
title_fullStr	Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials
title_full_unstemmed	Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials
title_sort	application of artificial neural networks in crystal growth of electronic and opto-electronic materials
publisher	MDPI AG
series	Crystals
issn	2073-4352
publishDate	2020-08-01
description	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.
topic	artificial neural networks crystal growth semiconductors oxides
url	https://www.mdpi.com/2073-4352/10/8/663
work_keys_str_mv	AT natashadropka applicationofartificialneuralnetworksincrystalgrowthofelectronicandoptoelectronicmaterials AT martinholena applicationofartificialneuralnetworksincrystalgrowthofelectronicandoptoelectronicmaterials
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Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

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