Silicon carbide (SiC) devices have shown definite advantages over Si counterparts in high-temperature, high-voltage, and high-frequency applications. To fully exploit the potentiality of SiC devices in high temperatures, die-attach materials that can withstand high temperatures for a long time are required in the power electronics packaging. In this article, the high-temperature die-attach materials, such as high-temperature solders and transient liquid-phase bonding materials, were reviewed first. Then, metallic (mainly Ag and Cu) nanoparticles (NPs) sintering technologies were thoroughly overviewed. The metallic NPs sintering materials, metallic NPs sintering process, and interface and reliability were analyzed, respectively. Finally, the challenges and outlook of promising Cu NPs sintering technology were discussed.

This paper focuses on characterizing the evolution of warpage, effects of epoxy molding compound (EMC), and effects of carrier 2 (the second carrier in the process) of 12 inch RDL-first multi-die fan-out wafer-level packaging (FOWLP) during the manufacturing process. The linear viscoelasticity properties of EMC and polyimide (PI) were characterized using dynamic mechanical analysis (DMA) in the frequency domain at different temperatures., The elastic and viscoelastic model were used for PI and EMC, the finite element analyses (FEA) of the cured structure were carried out and the results were compared with the test results. The viscoelastic properties of the EMC in the FEA could predict the wafer warpage more accurately. The FEA and experiments were used to investigate the evolution of warpage. The molding had a great influence on the warpage. The effects of the EMC and carrier 2 were also investigated with FEA. The wafer warpage could be reduced by lowering the thickness of the EMC, increasing the thickness of carrier 2, and selecting EMC and carrier 2 with a matched coefficient of thermal expansion (CTE).

Fan-out wafer-level packaging (FOWLP) is one of the most popular microelectronics packaging technologies. Redistribution layers (RDLs) are employed to expand chip pads beyond the chip edges. Photosensitive polyimide (PI) is widely used in RDLs fabrication. But it also introduces some issues during the thermal cycling test or highly accelerated stress test, for example, warpage, peeling, blistering, and even cracking. In order to reduce these risks at the materials selecting stage, four common dielectric materials–PI A, B, C, and phenolic resin D were investigated in their thermomechanical properties and surface characteristics. And a design of the experiment on process improvement was conducted. The experimental results show that material A owning excellent elongation exceeding 30%, acceptable curing temperature below 260 °C, and low coefficient of thermal expansion (CTE) at 52 ppm/K is more suitable to be adapted as dielectric in RDLs compared with other materials chosen in this article.

In substrate fabrication, thermal mismatch between materials and structural configurations can readily induce warpage deformation, which severely impacts subsequent chip mounting and solder joint alignment processes, ultimately compromising the reliability of packaging integration. Therefore, investigating the effects of material types and structural layouts on warpage is essential for design optimization. However, traditional experimental approaches incur high costs, while case-by-case finite element method (FEM) modeling presents computational bottlenecks. This study proposes an artificial intelligence (AI)-based method to predict the warpage behavior of organic substrates with diverse material and structural configurations. Through material preparation, substrate manufacturing, and comprehensive characterization testing, process-matched material parameters and warpage data were obtained. Combined with thermomechanical simulation, a high-precision dataset comprising 972 cross-case studies was constructed, achieving a prediction error of less than 10%. Utilizing this dataset, the network architectures and hyperparameters of Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM) algorithms were optimized, and their performance was evaluated in terms of loss convergence, learning rate adaptability, training efficiency, and robustness. The results indicate that MLP demonstrates rapid loss decay, a training time of 135 seconds, a fitting slope of 0.98, minimal dependency on dataset size, and outperforms the other algorithms. This approach reduces warpage evaluation from traditional day scale to AI-driven second scale, facilitating data-driven rapid design iterations for multi-material variable-structure substrates.

This study investigated moisture diffusion and moisture concentration distribution in Embedded Component Packages (ECP). Three polymeric materials, Ajinomoto Buildup Film (ABF), Polyimide (PI), and Bismaleimide Triazine (BT) used commonly in EMC were characterized using Fick diffusion and dual Fick diffusion theories, respectively. The samples were subjected to a soaking environment of 85 °C/85% relative humidity. All the samples showed non-Fickian diffusion behaviors, particularly the BT sample. The dual stage models agreed well with experimental data and provide reasonable characterizations of the diffusive properties. Subsequently, a simulation method based on the dual stage model was proposed to replicate the distribution of moisture concentration within an ECP device. The accuracy of the simulations was verified through a heavy water moisture diffusion experiment that utilized isotope tracing techniques. The paper also found that kerf played a role in blocking moisture infiltration. This study presents a more accurate method for characterizing the hygroscopic behavior of polymeric materials and constructing a moisture diffusion model for ECP.