Epoxy molding compounds (EMCs) are commonly used in electronic products for chip encapsulation, but the moisture absorption of EMC can induce significant reliability challenges. In this study, the effects of hygrothermal conditions and structure parameters on moisture diffusion and the consequent influences (such as moisture content on die surfaces and stress distribution) on a system-in-package module have been systematically investigated by moisture–thermal–mechanical-coupled modeling. Hygroscopic tests were carried out on a new commercial EMC at 60 °C/60% RH and 85 °C/85% RH, followed by evaluations of diffusion coefficients by Fick’s law. It was found that the moisture diffusion coefficients and saturation concentrations at 85 °C/85% RH were higher than those at 60 °C/60% RH. From the modeling, it was found that the consequent maximum out-of-plane deformation and stress of the module at 85 °C/85% RH were both higher than those at 60 °C/60% RH. Influences of thicknesses of EMC and PCB on the moisture diffusion behavior have also been studied for design optimization. It was found that the maximum moisture concentration on die surfaces and resultant stress increased notably with thinner PCB, whereas the effects of EMC thickness were limited. This can be attributed to the comparison between the thicknesses of EMC and PCB and the shortest existing diffusion path within the module. These findings can provide helpful insights to the design optimization of electronic modules for hygrothermal conditions.

The reliability of the thyristor is directly related to the safe operation of the DC transmission system. A method for evaluating the state of thyristors based on kernel principal component analysis (KPCA) is proposed, which firstly considers the thyristor test data, operation records, maintenance history, appearance inspection information, states of other components and operating environment. A basic index system for evaluating the aging state of thyristor with 42 parameters is established. Next, a mathematical model was developed by Fisher Discriminant Analysis (FDA). The kernel function of the kernel principal components is then optimized by an improved particle swarm optimization (IPSO) algorithm. The improved KPCA is applied to extract key parameters from the base index system to obtain the reduced dimensional evaluation indicators. The obtained principal component factors are used to determine the weights of the fuzzy composite factors, which are applied for fuzzy evaluation of the thyristor. Finally, 20 thyristors are selected for experimental and theoretical calculations. The results show that the cumulative contribution of the first three principal component variables after dimensionality reduction reaches 93.76%, which is consistent with the state of the thyristor. Compared to the four existing evaluation methods, the results of the method proposed in this paper are more reasonable, which removes the influence of redundant indicators, reduces the amount of data, and provides a reference for the related research on thyristor state evaluation.