Residual stress and thermally induced warpage are critical reliability concerns in power electronic packaging, particularly when employing sintered copper nanoparticle (Cu NP) interconnects. While these interconnects provide high thermal and electrical performance, they also introduce significant interfacial stresses during bonding that alter mechanical behavior during service. This study develops an integrated confocal Raman–analytical modeling framework to directly quantify and mechanistically interpret these stresses in a representative SiC/Sintered Cu NPs/Active Metal Brazing ceramic substrate (AMB) stack. Raman spectroscopy reveals a compressive interfacial stress field peaking at −334 MPa near the chip center with localized hotspots linked to microporosity. Complementary in-situ Moiré interferometry tracked warpage evolution during thermal cycle (30–310°C). Coupling this stress measurement with a three-dimensional thermoelastic model, and explicitly assigning the bonding temperature as the stress-free reference, enables accurate reproduction of the temperature-dependent warpage trajectory. The model predictions align with experimental interferometry within < 5% deviation. These results demonstrate that incorporating residual stress is essential to realistically capture thermo-mechanical evolution. The proposed Raman–model paradigm advances beyond prior purely numerical or purely experimental efforts by bridging quantitative stress mapping with predictive warpage modeling. This methodology provides essential insights for thermal-induced residual stress and warpage managements in high-temperature power electronics packaging, providing a stress-informed foundation for reliability assessment and design optimization.

Pressureless sintered silver paste is widely used in SiC power electronic packaging for its superior thermal and electrical properties, enabling efficient heat dissipation and improved device reliability. Current thermal conductivity models frequently assume isotropic thermal behavior to simplify heat transfer calculations, yet these models neglect the inherent anisotropic porosity of sintered silver materials. This omission introduces errors in the characterization of these materials' thermal performance. This research investigates how the spatial anisotropic distribution of pressureless sintered silver's microstructure impacts QFN packaging's overall thermal conduction performance. This investigation is achieved by comparing lateral/vertical porosity differences between two materials and applying multidimensional thermal conductivity modeling. Two types of pressureless sintered silver were employed as die-attach materials to fabricate SiC MOSFET-based QFN packaging. The sintered microstructures' lateral and vertical cross-sections were characterized using scanning electron microscopy (SEM), enabling quantitative extraction of anisotropic porosity distributions. Subsequently, a numerical model was developed using the extracted porosity data to enhance the accuracy of heat transfer predictions in sintered silver layers while considering anisotropic thermal conductivity. Thermal resistance characterization was conducted on two QFN packages, and the accuracy of the proposed modeling methodology was validated by establishing the interrelation between experimental thermal resistance measurements and theoretical thermal conductivity predictions. This study demonstrates a refined approach to evaluating and optimizing sintered silver materials, providing a more accurate and application-driven thermal management strategy for SiC MOSFET power packaging.

This study investigates the size-dependent mechanical behavior and deformation mechanisms of sintered copper (Cu) nanoparticles (NPs) through micro-pillar (2–6 μm diameter) compression tests, scanning electron microscopy (SEM), transmission electron microscopy (TEM), transmission Kikuchi diffraction (TKD) analysis and molecular dynamics (MD) simulations. In-situ micro-pillar compression tests reveal a 25.9% reduction in yield strength (812 ± 64 MPa to 643 ± 47 MPa) with increasing pillar size, attributed to dislocation starvation in smaller pillars and porosity-driven strain localization in larger ones. TKD quantifies dynamic grain refinement (24.9% reduction in grain size) and geometrically necessary dislocation (GND) density escalation (74.8%), driven by stress gradients and grain boundary-mediated plasticity. Nanoindentation-derived elastic modulus (48.3 ± 11.1 GPa) exceeds micropillar values (29.5–33.9 GPa), reflecting substrate constraints in bulk testing. Microstructural analysis identifies a transition from shear banding in high-porosity pillars to uniform plasticity in denser systems, mediated by texture evolution (Brass/S components) and Schmid factor redistribution (62% increase in high-slip-activity grains). MD simulations of pressure-sintered Cu NPs elucidate atomic-scale mechanisms: dislocation nucleation at sintering necks, pore collapse-induced strain localization, and grain boundary sliding. These findings establish a multiscale framework linking porosity, grain refinement, and dislocation dynamics to mechanical performance, emphasizing microstructural optimization for enhanced reliability in microelectronic applications. The integration of MD simulations bridges atomic-scale mechanisms to microscale deformation, providing actionable insights for tailoring sintered Cu NPs via reduced porosity and controlled grain boundary architectures.

Silicon carbide (SiC) MOSFETs, as leading wide bandgap semiconductor devices, exhibit superior stability and reliability under high-temperature, high-switching frequencies, and high-power density operational conditions. SiC MOSFET with fan-out panel-level packaging (FOPLP) utilizes a redistribution layer (RDL) to substitute bonding wires, achieving low thermal resistance, electrical resistance, and parasitic inductance. This study proposes an optimal design strategy for SiC MOSFET FOPLP, considering parasitic inductance suppression, heat dissipation, thermal-mechanical reliability, and insulation enhancements. First, we establish the parasitic inductance, heat transfer network, and thermomechanical stress physical analytical models of SiC MOSFET FOPLP. Subsequently, the non-dominated sorting genetic algorithm (NSGA-II) and the improved multi-objective particle swarm optimization algorithm (MOPSO) are integrated to realize the co-optimization of parasitic inductance, thermal resistance, thermal stress, and electric field intensity distribution. Finally, we attain the optimal parameters of the SiC MOSFET FOPLP, i.e. chip thickness (x₁), solder thickness (x₂), RDL thickness (x₃), and chip side length (x₄) as 0.25 mm, 0.05 mm, 0.35 mm, and 6.00 mm, respectively. Additionally, according to comparison the MOPSO algorithm exhibits faster convergence and superior diversity than NAGA-II in the electrical-thermal-mechanical multiphysics co-optimization. Generally, the proposed physical analytical models combined with a multi-objective optimization method have a substantial guidance and forward-looking prospect on the design of SiC MOSFET FOPLP.

As the next generation of semiconductor devices, SiC MOSFETs have demonstrated significant performance improvements in switching loss, switching frequency, and high-temperature operation compared to Si-based MOSFETs. However, the long-term reliability of such devices and their packaging continues to be a major concern. Towards addressing this challenge, this study proposes a multi-objective optimization design method for parasitic inductance (L), thermal strain (?), and thermal resistance (R) of SiC MOSFETs with Fan-Out Panel-Level Packaging (FOPLP). First, the orthogonal experimental design was employed to investigate the thickness effects of baseplate, solder, die and redistribution layer (RDL) on L, e, and R. Then, the multi-objective optimization was developed to simultaneously reduce L, G, and R. Finally the fatigue lifetimes of the optimized and initial SiC MOSFET FOPLP structures were compared to verify the optimization's accuracy. Study findings include: (1) Solder thickness was the most significant influence factor for L, e and R of SiC MOSFET FOPLP, L and R increased, and e decreased with increasing solder thickness; (2) The proposed multi-objective optimization method coupled with a genetic algorithm achieved 14.79, 8.96, and 9.28% reduction of L, e, and R, respectively; (3) The fatigue lifetime of solder (SAC305) was evaluated using the Coffin-Manson model, with predicted lifetimes before and after optimization being 6786 and 7085 cycles, respectively, demonstrating that the proposed approach significantly enhanced the designed SiC MOSFET FOPLP's long-term thermal cycling reliability.