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Reliable connections of electrical components embody a crucial topic in the microelectronics and power semiconductor industry. This study utilises 3D non-destructive X-ray tomography and specifically developed machine learning (ML-) algorithms to statistically investigate crack initiation and propagation in SAC305-Bi solder balls upon thermal cycling on board (TCoB). We quantitatively segment fatigue cracks and flux pores from 3D X-ray tomography data utilising a multi-level ML-workflow incorporating a 3D U-Net model. The data reveals that intergranular fatigue cracking is the predominant failure mechanism during TCoB and that dynamic recrystallisation precedes crack initiation. Moreover, we find that fatigue cracks are initiated at surface notches, flux pores and printed circuit board-metallisation intrusions. The work provides important insights regarding the underlying microstructural and mechanical mechanisms for recrystallisation and cracking, uniting the aspects of big-data analysis with ML-algorithms and in-depth understanding about the underlying materials science. [ABSTRACT FROM AUTHOR]

In the present study, the applicability of rapid flow injection-triple quadrupole mass spectrometry for simultaneous qualitative screening of different classes of stabilizers in polymeric materials is demonstrated. Electrospray ionization and atmospherical pressure chemical ionization were compared, whereby the latter yielded generally poorer detection limits and only single charged ions that were for some analytes beyond the mass range of the quadrupole mass spectrometry. Positive electrospray ionization allowed the interference-free monitoring of multiple reaction monitoring transitions selective for 36 commonly used stabilizers without chromatographic separation. Real polymer samples were extracted by toluene and the method allowed the detection of analytes down to 0.00001-0.025 wt% depending on the stabilizer. [ABSTRACT FROM AUTHOR]