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dc.contributor.authorJensen, Mathias Novik
dc.contributor.authorHellesø, Olav Gaute
dc.date.accessioned2022-08-08T09:02:16Z
dc.date.available2022-08-08T09:02:16Z
dc.date.issued2022-01-22
dc.description.abstractIn this work, three silicon samples are subject to tomographic scans using a 1.6μm laser. The samples were prematurely terminated due to anomalies during the Czhochralski-process. They are taken as analogues of the in situ crystal, where one sample has known aberrant structure in its lowermost 45 mm. The results of the tomographic scans show a distinct difference in transmission profile between the material of known poor monocrystalline structure and assumed good structure. Three different analysis tools are constructed and applied to quantify the quality of the structure from the results of the tomographic scans. The first two analysis tools are applied as correlation filters constructed from patterns resembling the indicative transmission profiles of highquality structure, one pattern being an ideal square wave and the other being experimentally determined from the measurements. Both correlation filters yield clear differentiation of low- vs. high-quality material. The final analysis tool is a deep convolutional neural network (deep CNN) evolved from a predetermined architecture configuration using a genetic algorithm. The trained CNN is shown to differentiate the usable high-quality material from the unusable material with a 98.7% accuracy on a testing set of 76 profiles and successfully assigns quality factors to the material that are in good agreement with the correlation filters and previous observations.en_US
dc.identifier.citationJensen, Hellesø. Evaluation of crystalline structure quality of Czochralski-silicon using near-infrared tomography. Journal of Crystal Growth. 2022;583en_US
dc.identifier.cristinIDFRIDAID 2024174
dc.identifier.doi10.1016/j.jcrysgro.2022.126527
dc.identifier.issn0022-0248
dc.identifier.issn1873-5002
dc.identifier.urihttps://hdl.handle.net/10037/26011
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofJensen, M.N. (2023). Raman-spectroscopy of extracellular vesicles and self-supervised deep learning. (Doctoral thesis). <a href=https://hdl.handle.net/10037/31854>https://hdl.handle.net/10037/31854</a>.
dc.relation.journalJournal of Crystal Growth
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleEvaluation of crystalline structure quality of Czochralski-silicon using near-infrared tomographyen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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