A meshless method is used to simulate the Fluid-Structure Interaction (FSI) in a micropump intended to treat nerve injury. Conventional meshbased methods can suffer from mesh deformation and quality issues, and find it difficult to track the fluid-structure interface. The Material Point Method (MPM) combines Lagrangian material points with an Eulerian computational grid, thereby avoiding any mesh related problems. To simulate the valve dynamics in the micropump, MPM was used to analyze the effect of the valve length on the behaviour of the pump. A longer valve length takes longer to open, as it sticks to the valve seat, meaning the pump needs to generate more pressure to open the valve. This contribution shows that MPM simulations can be used to optimize the valve design for implantable micropumps.

The demand for rapid advancement in AI, mobile and automotive markets is pushing the boundaries of electronic packaging, including heterogeneous integration, high-power packages, and large-die packaging. Against this backdrop, machine learning technologies emerge as dynamic tools for correlation building and classification, revolutionizing the traditional approaches to design, manufacturing, and testing in electronic packaging, as well as the Design for Reliability (DfR) methodologies.This paper reviews the most recent AI-assisted approach for electronic packaging and then focuses on the AI-assisted DfR (AI-DfR) approaches. Our examination reveals that AI methods have been adapted to meet the specific needs of electronic packaging. The industry’s anticipation for AI-DfR stems from its potential to address prevailing reliability design challenges, yet its multidisciplinary essence poses hurdles to swift progress. This review proposes future directions for AI-DfR’s development, spotlighting critical areas such as the quality and efficiency of finite element modeling, design and optimization of training models, selection of AI models, and maintenance and value enhancement strategies.

The ability to accurately predict the reliability and lifetime of electronics is of great importance to the industry. The failure of the solder joint is of particular interest for these predictions, because of their susceptibility to failure under thermo-mechanical stress. However, the experimental or even conventional simulation techniques employed to estimate the lifetime of a solder joint are often too expensive or time consuming to be of practical use. Therefore, this work introduces a physics-informed Long Short-Term Memory (LSTM) to predict the plastic strain in the critical area of the solder joint. The predicted values are in agreement with the values gained from finite elements, thereby demonstrating the advantage of applying the proposed methodology.