Deep Learning assisted quantitative Assessment of the Porosity in Ag-Sinter joints based on non-destructive acoustic inspection

Brand S, Koegel M, Altmann F, Bach L (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Proceedings - Electronic Components and Technology Conference Institute of Electrical and Electronics Engineers

Publisher: IEEE COMPUTER SOC

City/Town: LOS ALAMITOS

Pages Range: 877-884

Conference Proceedings Title: IEEE 71ST ELECTRONIC COMPONENTS AND TECHNOLOGY CONFERENCE (ECTC 2021)

Event location: , ELECTR NETWORK

DOI: 10.1109/ECTC32696.2021.00147

Involved external institutions

Fraunhofer-Institut für Mikrostruktur von Werkstoffen und Systemen IMWS DE Germany (DE) Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie (IISB) DE Germany (DE)

How to cite

APA:

Brand, S., Koegel, M., Altmann, F., & Bach, L. (2021). Deep Learning assisted quantitative Assessment of the Porosity in Ag-Sinter joints based on non-destructive acoustic inspection. In IEEE 71ST ELECTRONIC COMPONENTS AND TECHNOLOGY CONFERENCE (ECTC 2021) (pp. 877-884). , ELECTR NETWORK: LOS ALAMITOS: IEEE COMPUTER SOC.

MLA:

Brand, Sebastian, et al. "Deep Learning assisted quantitative Assessment of the Porosity in Ag-Sinter joints based on non-destructive acoustic inspection." Proceedings of the IEEE 71st Electronic Components and Technology Conference (ECTC), , ELECTR NETWORK LOS ALAMITOS: IEEE COMPUTER SOC, 2021. 877-884.

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