Success Story

Degradation Mechanisms in Sustainable Solders

The aspects of big-data machine learning algorithms are united with an in depth understanding of materials science to gain crucial insights into microstructural and mechanical mechanisms, which lead to cracking in microelectronic solders.

Using artificial intelligence to analyze microchips Image: MCL - AI-generated with Adobe Firefly.

Using artificial intelligence to analyze microchipsImage: MCL - AI-generated with Adobe Firefly.

Workflow of the ML-based method that has been developed. In the 3D X-ray tomography volume, individual solder balls are found using ML algorithms and then segmented. The method provides spatially resolved information about solder ball damage in entire electronic components. This statistical 3D damage information is then used to perform high-resolution root cause analyses. Image: MCL, Details can be found in: C. Cui et al., Mater Degrad 8, 40 (2024), https://www.nature.com/articles/s41529-024-00456-8

Workflow of the ML-based method that has been developed. In the 3D X-ray tomography volume, individual solder balls are found using ML algorithms and then segmented. The method provides spatially resolved information about solder ball damage in entire electronic components. This statistical 3D damage information is then used to perform high-resolution root cause analyses.Image: MCL, Details can be found in: C. Cui et al., Mater Degrad 8, 40 (2024), https://www.nature.com/articles/s41529-024-00456-8

In the microelectronics and power electronics industry, the reliability and service life of electrical connection technologies play a decisive role. Often dozens of solder balls are used to maintain the electrical connection between chip and PCB. The integrity of these solder balls is essential for the functionality of the electronic component.


In order to develop more sustainable solders for microelectronic devices, an in depth understanding of the degradation mechanisms in these interconnects is essential. However, understanding these mechanisms, especially in Lead free solder alloys, remains a vital challenge in the field of materials science.


Tin-based solders have largely replaced lead-based solders in the electronics industry due to health and environmental concerns. Nevertheless, there are aspects of fatigue and service life reduction with temperature changes of these tin-based solders that have not yet been fully researched scientifically. If bismuth is added, for example, the service life is significantly extended. This approach has the potential to significantly delay the microstructural degradation of these sustainable solders. In order to specifically investigate the effects on cracks and pores in various solder alloys after cyclic temperature cycling, a machine learning (ML)-based workflow was developed to visualize and quantify the damage in entire electronic components non-destructively and in 3D.


Impact and effects
The 3D visualization of the electronic components is carried out using non-destructive X-ray tomography. Different materials can be distinguished in the tomography volumes based on their different densities. In this way, tin solder balls can be distinguished from their air environment and from copper metallization, and pores and cracks in the solder can be identified. The developed ML model then identifies each individual solder ball in the tomography volume and a second model segments the cracks and pores present in the solder ball. 
The workflow therefore provides detailed, spatially resolved information about the solder damage in the component with little experimental effort.


The spatial resolution of the method enables both statistical and targeted root cause analysis. The microstructure modeling enables a detailed analysis of the detected phenomena in the solder joints. By examining the material structure on a microscopic level, the influences of various factors on the service life of the solder joints can be evaluated. These analyses provide important findings for the development of measures to extend the service life of solder joints.

 

Project Coordination (Story)
Priv.-Doz. Dr. Roland Brunner
Group leader material and damage analytics
Materials Center Leoben Forschung GmbH
roland.brunner(at)mcl.at



IC-MPPE / COMET-Zentrum
Materials Center Leoben Forschung GmbH
Vordernberger Straße 12
8700 Leoben
T +43 (0) 3842 45922-0
mclburo(at)mcl.at
www.mcl.at


Project Partners
 
•    Materials Center Leoben Forschung GmbH, Austria
•    KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH, Austria
•    Infineon Technologies AG, Germany
•    Montanuniversität Leoben, Austria

 

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