Surface mount technology (SMT) plays an important role in integrated circuits, but due to thermal stress alternation caused by temperature cycling, it tends to have thermo-mechanical reliability problems. At the same time, considering the environmental and health problems of lead (Pb)-based solders, the electronics industry has turned to lead-free solders, such as ternary alloy Sn-3Ag-0.5Cu (SAC305). As lead-free solders exhibit visco-plastic mechanical properties significantly affected by temperature, their thermo-mechanical reliability has received considerable attention. In this study, the interface delamination of an SMT solder joint using a SAC305 alloy under temperature cycling has been analyzed by the nonlinear finite element method. The results indicate that the highest contact pressure at the four corners of the termination/solder horizontal interface means that delamination is most likely to occur, followed by the y-direction side region of the solder/land interface and the top arc region of the termination/solder vertical interface. It should be noted that in order to keep the shape of the solder joint in the finite element model consistent with the actual situation after the reflow process, a minimum energy-based morphology evolution method has been incorporated into the established finite element model. Eventually, an Improved Efficient Global Optimization (IEGO) method was used to optimize the geometry of the SMT solder joint in order to reduce the contact pressure at critical points and critical regions. The optimization result shows that the contact pressure at the critical points and at the critical regions decreases significantly, which also means that the probability of thermal-induced delamination decreases.

With the demand for more powerful functions and miniaturization of electronic equipment, the requirements for complex interconnection design and manufacturing in electronic packaging are increasing. The pros and cons of interconnection configuration have a serious impact on its electrical performance, and have become a key factor affecting the development of complex interconnection in electronic packaging. In order to better study the influence mechanism of the configuration parameters of lead interconnection, a method for predicting the electrical performance of lead interconnection in electronic packaging based on configuration characteristics is presented. First, the configuration parameters of lead interconnection are extracted, then the parameterized characterization of lead interconnection is carried out. Taking the half-envelope rectangular lead interconnection as an example, the road coupling model of lead interconnection based on range analysis, variance analysis and response surface method is built and experimentally verified. Finally, the influence mechanism of the configuration characteristics of lead interconnect on electrical performance is analyzed. The work carried out provides theoretical guidance for the lead interconnection design and fast prediction of electrical signal transmission performance in electronic packaging.

Cerebral small vessel disease (CSVD) is a major condition with systemic and whole-brain effects, significantly impacting the daily lives of middle-aged and elderly people. Currently, predictions for CSVD are limited to traditional statistical methods and lack consideration of interactions between risk factors. Based on this, this study proposes an interpretable risk prediction method for CSVD based on combined motor function and interaction features. First, the proposed multidimensional feature selection method is applied for feature selection. Next, the RS+GWO+Voting method is proposed for model construction. Finally, the model constructed using SHapley Additive exPlanations (SHAP) is applied for interpretation, enhancing the clinical applicability of the model. The results show that the proposed model achieved a training set Area Under the Curve (AUC) of 0.829 and a validation set AUC of 0.824, demonstrating its strong predictive performance. The method provides theoretical support and technical backing for CSVD risk prediction under small-sample conditions and holds promise for integration with wearable devices to enable early warning and health management of CSVD. Received: 8 January 2026 | Revised: 31 March 2026 | Accepted: 15 April 2026 Conflicts of Interest Congsi Wang is an editorial board member for Smart Wearable Technology and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Benben Wang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Visualization. Yuefei Yan: Methodology, Writing – review & editing, Project administration. Huizhen Lu: Validation, Investigation, Data curation. Baoqing Han: Writing – review & editing, Project administration. Yuangen Deng: Software. Chengliang Zhang: Data curation. Jianwei Xu: Data curation. Jingtan Chen: Supervision. Chuanliu Wang: Resources, Supervision. Congsi Wang: Conceptualization, Formal analysis, Resources, Funding acquisition.