Machine learning assisted evaluation of the shape of VIAs in a LTCC multilayer
- The introduction of the 5G technology and automotive radar applications moving into higher frequency ranges trigger further miniaturization of LTCC technology (low temperature co-fired ceramics). To assess dimensional tolerances of inner metal structures of an industrially produced LTCC multilayer, computer tomography (CT) scans were evaluated by machine learning segmentation.
The tested multilayer consists of several layers of a glass ceramic substrate with low resistance silver-based vertical interconnect access (VIA). The VIAs are punched into the LTCC green tape and then filled with silver-based pastes before stacking and sintering. These geometries must abide by strict tolerance requirements to ensure the high frequency properties.
This poster presents a method to extract shape and size specific data from these VIAs. For this purpose, 4 measurements, each containing 3 to 4 samples, were segmented using the trainable WEKA segmentation, a non-commercial machine learning tool. TheThe introduction of the 5G technology and automotive radar applications moving into higher frequency ranges trigger further miniaturization of LTCC technology (low temperature co-fired ceramics). To assess dimensional tolerances of inner metal structures of an industrially produced LTCC multilayer, computer tomography (CT) scans were evaluated by machine learning segmentation.
The tested multilayer consists of several layers of a glass ceramic substrate with low resistance silver-based vertical interconnect access (VIA). The VIAs are punched into the LTCC green tape and then filled with silver-based pastes before stacking and sintering. These geometries must abide by strict tolerance requirements to ensure the high frequency properties.
This poster presents a method to extract shape and size specific data from these VIAs. For this purpose, 4 measurements, each containing 3 to 4 samples, were segmented using the trainable WEKA segmentation, a non-commercial machine learning tool. The dimensional stability of the VIA can be evaluated regarding the edge-displacement as well as the cross-sectional area. Deviation from the ideal tubular shape is best measured by aspect ratio of each individual layer. The herein described method allows for a fast and semi-automatic analysis of considerable amount of structural data. This data can then be quantified by shape descriptors to illustrate 3-dimensional information in a concise manner. Inter alia, a 45 % periodical change of cross-sectional area is demonstrated.…
Autor*innen: | Paul Geyler |
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Weitere Person: | Torsten Rabe, Björn MiellerORCiD, Fabien LéonardORCiD |
Dokumenttyp: | Posterpräsentation |
Veröffentlichungsform: | Präsentation |
Sprache: | Englisch |
Jahr der Erstveröffentlichung: | 2019 |
Organisationseinheit der BAM: | 5 Werkstofftechnik |
5 Werkstofftechnik / 5.4 Multimateriale Fertigungsprozesse | |
8 Zerstörungsfreie Prüfung | |
8 Zerstörungsfreie Prüfung / 8.5 Röntgenbildgebung | |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Freie Schlagwörter: | 5G; LTCC multilayer; Machine Learning |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Veranstaltung: | DKG Jahrestagung 2019 |
Veranstaltungsort: | Leoben, Austria |
Beginndatum der Veranstaltung: | 06.05.2019 |
Enddatum der Veranstaltung: | 08.05.2019 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 26.06.2019 |
Referierte Publikation: | Nein |