TY - GEN A1 - Geyler, Paul T1 - Machine learning assisted evaluation of the shape of VIAs in a LTCC multilayer N2 - 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. 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. T2 - DKG Jahrestagung 2019 CY - Leoben, Austria DA - 06.05.2019 KW - Machine Learning KW - LTCC multilayer KW - 5G PY - 2019 UR - https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/48289 AN - OPUS4-48289 AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany