In Case You Missed It
BGA Reliability
“Reliability Analysis and Parameter Optimization of Board-Level BGA Packaging Structures under Thermal-Drop Impact Load”
Authors: Yanxi Sun, Zixin Zhen, Xuexia Yang, Miao Zhu and Chao Chang
Abstract: As electronic products become more pervasive, instances of unintentional drops during periods of high-power consumption are increasingly prevalent. Thermal loads can induce fatigue damage in the solder joint material, whereas drop impact loads can instantaneously impose substantial mechanical stresses. Consequently, this study investigated the mechanical behavior of solder joints when subjected to the concurrent effects of heat and drop impact loads.
In this work, a three-dimensional finite element model of a board-level ball grid array (BGA) package structure is established, and numerical calculations are performed based on thermal-drop impact load sequence coupling. The effects of chip thickness, solder ball height, diameter and array on its temperature field distribution, solder ball stress and average impact life are investigated. Optimization schemes were designed using Taguchi quadrature and surface response methods. Mathematical-statistical analysis and regression analysis determined the optimal combination of structural parameters to minimize solder ball peeling stress. The results show that increasing the height and diameter of solder balls is beneficial in reducing package temperature under actual temperature loading induced by the chip power, and the temperature is lowest when the number of solder ball arrays is 12. Under the thermal-drop impact load sequence coupling, the maximum peeling stress value appears at the edge of the contact area between the tip of the outermost fillet ball and the substrate. The larger the height and smaller the diameter of the ball, the greater its ability to resist the drop impact load. The average collision life was 1.12 times longer at 0.34mm solder ball height than at 0.32mm, and 2.02 times longer at 0.56mm diameter than at 0.58mm. Under the same conditions, the optimization results of surface response method are better than Taguchi orthogonal method, and the optimal parameter combinations are chip thickness 0.285mm, solder ball diameter 0.56mm, height 0.325mm and pitch 0.592mm. The maximum peeling stress is reduced 19.5% compared with the pre-optimization period, and the average collision lifetime is increased 1.98 times, which realizes the optimization of structural parameters of the board-level BGA package. (Soldering & Surface Mount Technology, Jun. 16, 2025, https://www.emerald.com/insight/content/doi/10.1108/ssmt-01-2025-0003/full/html)
Circularity
“Liquid Metal-Vitrimer Conductive Composite for Recyclable and Resilient Electronics”
Authors: Dong Hae Ho, et al.
Abstract: Poor recycling rates of electronic devices contribute to substantial economic losses and worsening environmental impacts from electronic waste (e-waste) disposal. Here, recyclable and healable electronics are reported through a vitrimer-liquid metal (LM) microdroplet composite. These electrically conductive, yet plastic-like composites display mechanical qualities of rigid thermosets and recyclability through a dynamic covalent polymer network. The composite exhibits a high glass transition temperature, good solvent resistance, high electrical conductivity, and recyclability. The vitrimer synthesis proceeds without the need for a catalyst or a high curing temperature, which enables facile fabrication of the composite materials. The as-synthesized vitrimer exhibits a fast relaxation time with reconfigurability and shape memory. The electrically conductive composite exhibits high electrical conductivity with LM volume loading as low as 5 vol.%. This enables the fabrication of fully vitrimer-based circuit boards consisting of sensors and indicator LEDs integrated with LM-vitrimer conductive wiring. Electrical self-healing and thermally triggered material healing are further demonstrated with the composites. The vitrimer and LM-composite provide a pathway toward fully recyclable, mechanically robust, and reconfigurable electronics, thus advancing the field of electronic materials. (Advanced Materials, Jun. 1, 2025, https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202501341)
Imaging
“Reconstruction via mmWave Surface Normal Estimation”
Authors: Laura Dodds, et al.
Abstract: The authors present the design, implementation and evaluation of mmNorm, a new method for non-line-of-sight 3D object reconstruction using millimeter wave (mmWave) signals. In contrast to past approaches for millimeter-wave-based imaging that perform back projection for 3-D object reconstruction, mmNorm reconstructs the surface by estimating the object’s surface normals. To do this, it introduces a novel algorithm that directly estimates the surface normal vector field from mmWave reflections. By then inverting the normal field, it can reconstruct structural iso surfaces, then solve for the exact surface through a novel mmWave optimization framework. The authors built an end-to-end prototype of mmNorm using a TIIWR1443 Boost mmWave radar and a UR5e Robotic Arm and evaluated it in over 110 real-world experiments across more than 60 different everyday objects (covering most of the standard YCB dataset). In an a) mmWave Reconstruction Setup RGB-D Camera (Ground Truth) 77Ghz Radar b) Ground Truth Hidden Target Object c) Classical Reconstruction Object To Reconstruct d) mmNorm’s Reconstruction head-to-head comparison with state-of-the-art baselines, mmNorm achieves 96% reconstruction accuracy (3D F-score) compared to 78% for the best-performing baseline. The results show that mmNorm is capable of high-accuracy mmWave object reconstruction. (Massachusetts Institute of Technology, June 2025, https://www.mit.edu/~fadel/papers/mmNorm-paper.pdf)
Solder Joint Reliability Modeling
“A Machine Learning Framework with Shapley’s Additive Explanations to Assess Solder Joint Reliability for Electronic Packaging”
Authors: Qais Qasaimeh, Haoran Li, Saad Hamasha and Jia Liu
Abstract: Assessing solder joint reliability is a significant challenge in the electronics manufacturing industry, as numerous factors affect integrity and performance. Traditionally, accelerated life tests (ALTs) are used to evaluate solder joint reliability, and survival analysis models such as Weibull and the Cox proportional hazards model (Cox-PHM) are widely used to develop life prediction models based on ALT data. Machine learning (ML) models, including random survival forest, extreme gradient boosting (XGB), and survival support vector machines (SSVMs), offer promising data-driven alternatives, especially given their potential for higher predictive accuracy. However, their interpretability remains a concern for the electronics manufacturing community. In this study, the authors conducted systematic research to integrate multiple ML algorithms and Shapley’s additive explanation (SHAP) techniques to model solder joint reliability in thermal cycling tests from various impacting factors and to extract knowledge from the ML models for interpretability. The ML approaches demonstrate superior predictive performance compared to traditional survival analysis models. For instance, XGB achieves the highest c-index of 0.88 on the testing dataset, indicating strong discriminative power. Similarly, the KSSVM model yields the lowest test MAPE of 15.26%, reflecting excellent accuracy in predicting cycles to failure. The GB model also performs well, with a c-index of 0.88 and test MAPE of 15.31%, highlighting the reliability of boosting-based approaches. While traditional models like Cox-PHM and Weibull yield c-indices around 0.87 and 0.85, respectively, they fall short in prediction error, with MAPEs exceeding 20%. These findings confirm the advantages of advanced ML models in capturing complex patterns in reliability data. Furthermore, SHAP analysis enhances model transparency by revealing how critical features – such as component type, solder material, and aging duration – interact to drive failure predictions, offering insight beyond what conventional models can provide. (Journal of Electronic Materials, Jul. 10, 2025, https://link.springer.com/article/10.1007/s11664-025-12101-4)