Correlative, ML-based and non-destructive 3D-analysis of intergranular fatigue cracking in SAC305-Bi solder balls

Abstract

AbstractReliable 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.