Eintrag in der Universitätsbibliographie der TU Chemnitz
Schlosser, Tobias ; Beuth, Frederik ; Friedrich, Michael ; Kowerko, Danny
A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks
| Universität: | Technische Universität Chemnitz | |
| Förderung: | BMBF | |
| Institut: | Juniorprofessur Media Computing | |
| Dokumentart: | Konferenzbeitrag, referiert | |
| ISBN/ISSN: | Electronic ISBN: 978-1-7281-0303-7 ; USB ISBN: 978-1-7281-0302-0 ; Print on Demand(PoD) ISBN: 978-1-7281-0304-4 ; Electronic ISSN: 1946-0759 Print on Demand(PoD) ; ISSN: 1946-0740 | |
| DOI: | doi:10.1109/etfa.2019.8869311 | |
| URL/URN: | https://ieeexplore.ieee.org/document/8869311 | |
| Quelle: | 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 10.09.2019 - 13.09.2019, pp. 1511-1514. - IEEE, 2019 | |
| Freie Schlagwörter (Englisch): | Visualization , Inspection , Image processing , Convolutional neural networks , Process control , Manufacturing processes , computer vision , cost reduction , fault diagnosis , inspection , laser beam cutting , learning (artificial intelligence) , manufacturing processes , neural nets , production engineering computing , quality control , semiconductor industry |
