In Case You Missed It
BGA Inspection
“Structure Light Measurement for a BGA Packaged Chip with High-Precision and Large Field-of-View”
Authors: Jianfeng Zheng, et al.
Abstract: Ball grid array (BGA) packaging is one of the mainstream chip packaging modes. The deviation of solder ball height can significantly affect the safety and reliability of packaged chips and may even lead to chip failure. To efficiently and accurately measure the height of solder balls on large-sized square BGA chips, a large field-of-view (FOV) and high-precision structured light measurement system is constructed in this paper. The influence of fringes with different contrast and gamma nonlinearity on three-dimensional (3-D) reconstruction height is simulated to determine the boundary conditions of fringe quality. Based on these boundary conditions, the projection clarity and spot shape of the oblique projection lens are optimized according to the Scheimpflug principle. This effectively solves the problems of an unbiased axis and low utilization of projection in traditional structured light projection lenses and enables high-precision fringe projection on large-sized square BGA chips. Aiming at the abnormal height measurement problems caused by the high reflectivity and occlusion of the solder ball, an improved fusion algorithm is proposed, which can accurately fuse height data from multi-directional 3-D reconstruction data. The measurement system has an FOV of 95mm x 95 mm, a measurement accuracy of 1.4µm for a standard plane, and 4.7µm for BGA solder balls. The results show that this system can perform high-precision detection of the height of solder balls on BGA chips, especially large-sized square chips. (Applied Optics, August 2025, https://www.researchgate.net/publication/393886013_Structure_light_measurement_for_a_BGA_packaged_chip_with_high-precision_and_large_field-of-view)
Plasma Jet Printing
“Low-Temperature Deposition of Gold Patterns with Improved Adhesion and Conductivity Characteristics for Printed Electronic Applications”
Authors: Lakshmi Prakasan, et al.
Abstract: Development of efficient metal deposition methods for patterning and depositing metal structures is crucial for advancing electronics manufacturing. Existing multistep processes that require separate equipment for each step hinder the progress of scalable and rapid metal deposition techniques. Plasma jet printing (PJP) is an advanced printing technique that has the capability to deposit plasma-assisted sintered metal traces with improved adhesion with the help of a dielectric discharge plasma. In this work, the authors conducted a comprehensive study of the effect of plasma parameters on improving the surface properties of the substrate and electrical performance. The findings demonstrate a 6x improvement in the adhesion strength and a resistivity of 1.75 × 10–6Ωm achieved through low-temperature plasma sintering, making it suitable for depositing conductive traces on low-temperature substrates. The authors also demonstrate the heating-assisted plasma sintering to sinter metal nanoparticle inks containing PVP efficiently, significantly reducing the thermal budget while maintaining a single-step process. These results highlight PJP as a promising alternative to conventional metal deposition methods, offering a streamlined approach to high-performance electronic applications. (ACS Applied Materials & Interfaces, vol. 17, no. 32, Jul. 29, 2025, https://pubs.acs.org/doi/10.1021/acsami.5c10083)
Solder Joint Reliability
“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 the reliability of solder joints is a significant challenge in electronics manufacturing, as numerous factors affect integrity and performance. Traditionally, accelerated life tests (ALTs) are used for evaluating 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. The rise of machine learning (ML) models, including random survival forest, extreme gradient boosting (XGB), and survival support vector machines (SSVMs), offers 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)
Sustainability
“DissolvPCB: Fully Recyclable 3D-Printed Electronics with Liquid Metal Conductors and PVA Substrates”
Authors: Zeyu Yan, Su Hwan Hong, Josiah Hester, Tingyu Chen and Huaishu Peng
Abstract: DissolvPCB is a novel 3-D printing-based method to fabricate fully recyclable electronic circuits. The technique combines polyvinyl alcohol (PVA) substrates and eutectic gallium–indium (EGaIn) liquid metal to create printed circuit board assemblies (PCBAs) that can be easily dissolved and reassembled, significantly reducing electronic waste.
Unlike traditional FR-4-based PCBs, which are difficult to recycle and typically require industrial-scale processes to reclaim materials, the novel circuits can be immersed in water to separate components, reclaim liquid metal, and regenerate PVA filament. This approach enables makerspaces and prototyping labs to close the loop on small-batch electronics manufacturing.
The novel approach relies on FDM 3-D printing with PVA filament to form circuit substrates containing hollow channels for EGaIn injection. The researchers developed a FreeCAD plugin that automatically converts KiCad PCB designs into 3-D-printable models with integrated sockets for through-hole and surface-mounted components. The additive process supports 3-D circuit topologies and shape-changing devices using Joule heating, as demonstrated with a self-bending gripper. The circuits demonstrated reliable performance, supporting currents up to 5A and high-frequency signals up to 10MHz, making them suitable for a range of prototyping applications. In testing, component recovery rates were up to 99.4% for PVA and 98.6% for liquid metal, and PVA was successfully re-extruded into new filament after dissolution. A lifecycle assessment (LCA) showed that DissolvPCB substantially outperformed traditional CNC-milled FR-4 boards across eight environmental metrics, including global warming potential, acidification potential and resource depletion. (UIST 2025 Proceedings, September 2025, https://uist.acm.org/2025/)