Gast ::
| Hauptseite > Publikationsdatenbank > Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer > print |
| Hauptseite > Publikationsdatenbank > Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer > print |
| 001 | 1026670 | ||
| 005 | 20250204113857.0 | ||
| 024 | 7 | _ | |a 10.1557/s43579-024-00563-2 |2 doi |
| 024 | 7 | _ | |a 2159-6859 |2 ISSN |
| 024 | 7 | _ | |a 2159-6867 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-03488 |2 datacite_doi |
| 024 | 7 | _ | |a WOS:001233735600001 |2 WOS |
| 037 | _ | _ | |a FZJ-2024-03488 |
| 082 | _ | _ | |a 670 |
| 100 | 1 | _ | |a Nguyen, Binh Duong |0 P:(DE-Juel1)187067 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer |
| 260 | _ | _ | |a Berlin |c 2024 |b Springer |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1724398891_22840 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Detecting and analyzing various defect types in semiconductor materials is an important prerequisite for understanding the underlying mechanisms and tailoring the production processes. Analysis of microscopy images that reveal defects typically requires image analysis tasks such as segmentation and object detection. With the permanently increasing amount of data from experiments, handling these tasks manually becomes more and more impossible. In this work, we combine various image analysis and data mining techniques to create a robust and accurate, automated image analysis pipeline for extracting the type and position of all defects in a microscopy image of a KOH-etched 4H-SiC wafer. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Steiner, Johannes |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Wellmann, Peter |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Sandfeld, Stefan |0 P:(DE-Juel1)186075 |b 3 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1557/s43579-024-00563-2 |0 PERI:(DE-600)2645443-9 |p 612-627 |t MRS communications |v 14 |y 2024 |x 2159-6859 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1026670/files/s43579-024-00563-2.pdf |
| 856 | 4 | _ | |y OpenAccess |x icon |u https://juser.fz-juelich.de/record/1026670/files/s43579-024-00563-2.gif?subformat=icon |
| 856 | 4 | _ | |y OpenAccess |x icon-1440 |u https://juser.fz-juelich.de/record/1026670/files/s43579-024-00563-2.jpg?subformat=icon-1440 |
| 856 | 4 | _ | |y OpenAccess |x icon-180 |u https://juser.fz-juelich.de/record/1026670/files/s43579-024-00563-2.jpg?subformat=icon-180 |
| 856 | 4 | _ | |y OpenAccess |x icon-640 |u https://juser.fz-juelich.de/record/1026670/files/s43579-024-00563-2.jpg?subformat=icon-640 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1026670 |p openaire |p open_access |p OpenAPC_DEAL |p driver |p VDB |p openCost |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)187067 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)186075 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 915 | p | c | |a APC keys set |2 APC |0 PC:(DE-HGF)0000 |
| 915 | p | c | |a Local Funding |2 APC |0 PC:(DE-HGF)0001 |
| 915 | p | c | |a DFG OA Publikationskosten |2 APC |0 PC:(DE-HGF)0002 |
| 915 | p | c | |a DEAL: Springer Nature 2020 |2 APC |0 PC:(DE-HGF)0113 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2023-08-26 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2023-08-26 |
| 915 | _ | _ | |a DEAL Springer |0 StatID:(DE-HGF)3002 |2 StatID |d 2023-08-26 |w ger |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2024-12-17 |w ger |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b MRS COMMUN : 2022 |d 2024-12-17 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2024-12-17 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2024-12-17 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2024-12-17 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1150 |2 StatID |b Current Contents - Physical, Chemical and Earth Sciences |d 2024-12-17 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2024-12-17 |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2024-12-17 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-9-20201008 |k IAS-9 |l Materials Data Science and Informatics |x 0 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-9-20201008 |
| 980 | _ | _ | |a APC |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|