Technical Abstracts

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“Advanced Characterization of a Hybrid Shielding Solution for Reducing Electromagnetic Interferences at Board Level”

Authors: Jorge Victoria, et. al.

Abstract: The development of new advanced functionalities, miniaturization, and the aim of obtaining optimized performance in electronic devices significantly impacts their electromagnetic compatibility (EMC). As electronic components become more densely packed on a printed circuit board (PCB), unintended coupling between components can cause electromagnetic interference (EMI). These requirements result in design restrictions that make using a board-level shield (BLS) essential in reducing intra-system EMI in PCB designs. This contribution focuses on studying and characterizing a BLS solution based on combining a noise suppression sheet (NSS) with an aluminum layer to reduce intra-system EMI coupling. This hybrid solution has the advantage of providing a shielding option that does not require any electronic redesign. It does not need a footprint or a ground connection as it can be affixed over the EMI source. The solution is expected to provide higher attenuation levels than using only an NSS by combining the absorbing properties of the magnetic material and the loss mechanism of the metal. To verify the effectiveness of the hybrid BLS proposed solution, the magnetic near-field emissions of an EMI source are analyzed in this study. The experimental measurements and simulated results demonstrate a significant increase (51.6dB at 1GHz) in the shielding effectiveness (SE) provided by the proposed solution compared to a conventional NSS. (Electronics, January 2024,

Sn-Bi Alloys

“Investigating the Effects of Rapid Precipitation of Bi in Sn on the Shear Strength of BGA Sn-Bi Alloys”

Authors: Qichao Hao, et. al.

Abstract: The potential of SnBi alloys as low-temperature solders for electronics manufacturing has spurred significant research on their mechanical properties, both in the as-soldered condition and after aging. Previous studies have demonstrated that, because of the extreme temperature sensitivity of the solubility of Bi in Sn, the mechanical properties of SnBi solder alloys are very sensitive to their thermal history. While the properties of the bulk solder alloy are a factor in its performance as a solder joint, the reliability in service is also affected by joint geometry and the interaction of the solder alloy with the joint substrate. This work assesses the effect of thermal history on solder joints formed with representative SnBi alloy solder balls by measuring the performance in a standard ball shear test of a solder ball reflowed to solder mask-defined (SMD) copper pads with organic solderability preservative (OSP) or electroless nickel/immersion gold (ENIG) finishes. The solder ball/substrate combinations were tested within 10 min. of reflow and after room-temperature storage for up to 10 days to determine the effect of aging on their response to the ball shear test. The results show that the peak force and fracture mode of SnBi solder joints is influenced by the SnBi alloy composition, the substrate type, and the aging time. These observations provide new information on the capability of these alloys to deliver reliable service over a range of operating conditions. (Journal of Electronic Materials, Dec. 2023,

Solder Reliability

“Temperature And Current Density Prediction in Solder Joints Using Artificial Neural Network Method”

Authors: Yang Liu, et. al.

Abstract: Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing product reliability. This study proposes a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and provides reference information for evaluation of solder joint reliability. (Soldering & Surface Mount Technology, December 2023,

Thermal Conductivity

“End-To-End Material Thermal Conductivity Prediction Through Machine Learning”

Authors: Yagyank Srivastava and Ankit Jain

Abstract: The authors investigated the accelerated prediction of the thermal conductivity of materials through end-to-end structure-based approaches employing machine learning methods. Due to the non-availability of high-quality thermal conductivity data, the authors first performed high-throughput calculations based on first principles and the Boltzmann transport equation for 225 materials, effectively more than doubling the size of the existing dataset. The authors assessed the performance of state-of-the-art machine learning models for thermal conductivity prediction on this expanded dataset and observed that all these models suffered from overfitting. To address this issue, the authors introduced a different graph-based neural network model, which demonstrated more consistent and regularized performance across all evaluated datasets. Nevertheless, the best mean absolute percentage error achieved on the test dataset remained in the range of 50–60%. This suggests that while these models are valuable for expediting material screening, their current accuracy is still limited. (Journal of Applied Physics, December 2023, ending bug