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.

This work aims to show Nb micro-alloying role on structural changes and glass forming ability (GFA) of Zr55Cu30Ni5Al10 bulk metallic glass. Pair distribution function (PDF) analysis indicates that the minor Nb addition forms Nb-centered clusters at the short range scale. It is also revealed that the atomic rearrangement happens in the medium range order and loosely-packed structure changes to a densely-packed one. This means that the clusters tend to form interpenetrating structures at the medium range scale. This event is related to the strong interaction of Zr and Nb elements in the material. Moreover, the thermal analyses demonstrate that the sample with Nb has a greater super-cooled liquid region and better GFA. The relaxation enthalpy decreases in the Nb-content sample which is due to the formation of denser clusters at short range scale and a structural shrinkage at the medium range order of the bulk metallic glass.

There exists an extensive range of applications for elevated gain DC/DC converters, as numerous low-voltage resources are exploited for power supply. Therefore, this study introduces a groundbreaking magnetically coupled DC/DC converter specifically designed for resources with low voltage, including micro PV or fuel cell systems. By enduring low current and voltage stresses, the power devices in this converter ensure remarkable efficiency while maintaining proven voltage ratio capability. The operational principles of the converter are thoroughly discussed and supported by the implementation of a 200W-400V prototype. In order to confirm the effectiveness of the converter, a range of experimental tests were carried out in different scenarios, confirming its dependable functionality. This novel converter opens up new possibilities for harnessing the potential of low-voltage resources, offering an efficient and reliable solution for power conversion. The results obtained from this research make a significant contribution to the progress of renewable energy systems by demonstrating the practicality and efficiency of the suggested converter in real-life situations.