Journal
IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY
Volume 12, Issue 2, Pages 349-358Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCPMT.2021.3136751
Keywords
Soldering; Predictive models; Training; Reliability; Geometry; Thermal loading; Prediction algorithms; Creep damage; electronic device; fatigue damage; iterative machine learning; solder joint
Categories
Funding
- Reliable Power Electronics-Based Power System (REPEPS) Project at the Department of Energy Technology, Aalborg University, Villum Investigator Program - Villum Foundation
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In this article, an iterative machine learning framework is designed to predict the useful lifetime of solder joints. The effects of solder alloy and solder layer geometry on the creep-fatigue damage evolution of the solder joint are evaluated separately. The proposed framework significantly improves the accuracy of solder joint lifetime prediction in a short time.
Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this article, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforce the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network (CDNN) to fulfill the data diversity. Outcomes show a very significant enhancement in lifetime prediction accuracy of the solder joint within a very short time. The effects of solder alloy and solder layer geometry are separately evaluated on the creep-fatigue damage evolution of the solder joint. The results reveal that Sn-Ag-Cu-based solder alloy generally has a better performance. Moreover, the creep and fatigue damage evolutions are found dominant, respectively, in Sn-Pb- and Sn-Ag-Cu-based solder alloys. The proposed framework offers a tool allowing for the reliability-driven design of electronic devices in the early stage of fabrication.
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