Automatized failure analysis of tungsten coated TSVs via scanning acoustic microscopy |
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| Affiliation: | 1. Materials Center Leoben Forschung GmbH (MCL), Leoben, Austria;2. PVA TePla Analytical System AG (PVA TePla), Aalen, Germany;3. ams AG, Unterpremstätten, Austria;1. Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy;2. Intraspec Technologies, 3 avenue Didier Daurat, 31400 Toulouse, France;3. CNES, 18 avenue Edouard Belin, 31401 Toulouse Cedex 9, France;1. Dept. of Electrical Engineering and Information Technologies, University Federico II, Naples, Italy;2. Vishay Semiconductor Italiana, Borgaro, Torinese (TO), Italy;3. DISAT Dept. of Applied Science and Technology, Politecnico di Torino, Italy;1. Kleindiek Nanotechnik, Aspenhaustr. 25, 72770 Reutlingen, Germany;2. Global Foundries, Wilschdorfer Landstraße 101, 01109 Dresden, Germany;1. Le2i, UMR CNRS 6306, Univ. Bourgogne Franche-Comté, 9 Avenue Alain Savary, 21000 Dijon, France;2. Centre National d''Etudes Spatiales (CNES), 18 Avenue Edouard Belin, 31401 Toulouse, France;3. Temasek Laboratories, Nanyang Technological University, 50 Nanyang Drive, Singapore |
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| Abstract: | In 3D integrated microelectronics, the failure analysis of through silicon vias (TSVs) represents a highly demanding task. In this study, defects in tungsten coated TSVs were analysed using scanning acoustic microscopy (SAM). Here, the focus lay on the realization of an automatized failure detection method towards rapid learning. We showed that by using a transducer of 100 MHz center frequency, established with an acoustical objective (AO), it is possible to detect defects within the TSVs. In order to interpret our analysis, we performed acoustic wave propagation simulations based on the elastodynamic finite integration technique (EFIT). In addition, high resolution X-ray computed tomography (XCT) was performed which corroborated the SAM analysis. In order to go towards automatized defect detection, firstly the commercially available software “WinSAM8” was enhanced to perform scans at defined working distances automatically. Secondly, a pattern recognition algorithm was successfully applied using “Python” to the SAM scans in order to distinguish damaged TSVs from defect-free TSVs. Besides the potential for automatized failure detection in TSVs, the SAM approach exhibits the advantages of fast and non-destructive failure detection, without the need for special preparation of the sample. |
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