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

dc.bibliographicCitation.firstPage663eng
dc.bibliographicCitation.issue8eng
dc.bibliographicCitation.journalTitleCrystalseng
dc.bibliographicCitation.volume10eng
dc.contributor.authorDropka, Natasha
dc.contributor.authorHolena, Martin
dc.date.accessioned2021-09-27T12:30:49Z
dc.date.available2021-09-27T12:30:49Z
dc.date.issued2020
dc.description.abstractIn 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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/6916
dc.identifier.urihttps://doi.org/10.34657/5963
dc.language.isoengeng
dc.publisherBasel : MDPIeng
dc.relation.doihttps://doi.org/10.3390/cryst10080663
dc.relation.essn2073-4352
dc.rights.licenseCC BY 4.0 Unportedeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.subject.ddc540eng
dc.subject.otherArtificial neural networkseng
dc.subject.otherCrystal growtheng
dc.subject.otherOxideseng
dc.titleApplication of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materialseng
dc.typeArticleeng
dc.typeTexteng
tib.accessRightsopenAccesseng
wgl.contributorIKZeng
wgl.contributorLIKATeng
wgl.subjectChemieeng
wgl.typeZeitschriftenartikeleng
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