Cu-Ag composite sintered pastes are promising die-attach materials for wide bandgap power modules, yet their electrothermal performance is governed by 3D phase connectivity and interfacial morphology. In this work, a workflow for better predicting the thermal and electrical conductivity was presented, using 2D SEM processing, 3D reconstruction with a self-learning optimized QSGS algorithm, and electrothermal finite element simulations in COMSOL Multiphysics. A Hashin Shtrikman composition baseline, together with closed-form structure-corrected equations, was further introduced for better prediction accuracy, which enables rapid screening and microstructure-informed design of composite sintering materials for die-attach.

We report a bioinspired, transparent green plant-like window that integrates passive cooling, thermal insulation, and solar-driven waste heat recovery to reduce building energy consumption. By mimicking leaf transpiration, the system uses a carbon quantum dot-doped CPPB hydrogel to achieve high visible transmittance (~92%) while blocking UV and NIR radiation. A self-powered water circulation layer repurposes solar heat for domestic water heating. Outdoor field tests and 168-h solar simulator exposure confirm long-term thermal stability and cooling up to 21.9°C. Simulations across 30 global cities show annual cooling energy savings up to 569.1 MJ/m ². This scalable, multifunctional window offers a sustainable path toward net-zero energy buildings.

Low-temperature atmospheric plasma (LTP) is widely used in industrial processes, such as disinfection, surface modification and wastewater treatment. The dielectric barrier discharge (DBD) is regarded as one of the most robust and reliable methods for generating LTP in ambient air. Compared to conventional AC excitation, pulsed powering offers several advantages (i.e., lower energy use and heat production). The present trend is to use short and fast pulses (in the nano- and picosecond range). In this review, the key design parameters of a DBD (barrier thickness, relative permittivity and gap distance) are discussed. Material-specific phenomena like surface charging and degradation are analyzed. The complex interactions between the pulse source and DBD are examined. By mapping the interdependencies, this review aims to support the rational design and optimization of pulsed DBD systems, and to facilitate their broader industrial use.

Pressureless sintered Ag pastes are promising die-attach materials for power electronics, yet practical sintering-profile optimization still relies heavily on trial-and-error, and the link from thermal kinetics to fracture-relevant microstructure and strength remains insufficiently established. In this work, a thermal-kinetics-guided workflow was developed to design paste-specific pressureless sintering profiles for two commercial Ag pastes (a spherical-particle paste and a flake-based paste) and to interpret the resulting strength–fracture response using SEM-based, heterogeneity-aware learning. Multi-heating-rate TGA was used to define the conversion fraction (α), while DSC was used to identify the dominant thermal-event window and extract the characteristic peak temperature for Arrhenius pairing. The TGA-defined conversion evolution was then modeled using a JMAK/Avrami form with Arrhenius temperature dependence to predict the isothermal holding time required to reach a target conversion at a selected dwell temperature. Relative to supplier-recommended profiles, the optimized profiles increased die-shear strength by 35% for the spherical-particle paste and by 206% for the flake-based paste, and promoted a fracture-mode transition from interfacial debonding toward mixed/cohesive fracture. Pearson/Ridge baselines and attention-based multiple-instance learning (MIL) linked strength and fracture-mode distribution to porosity/connectivity-related descriptors; paste-wise normalization mitigated paste-specific baselines and enabled MIL to reveal profile-induced microstructure–performance co-variation. Overall, this study establishes a practical workflow that couples thermal-kinetics-guided profile design with mechanical and fractographic validation and interpretable microstructure–property attribution, supporting mechanism-informed optimization of pressureless sintered Ag die-attach pastes.

The rising demand for high-power semiconductor devices in sectors such as electric vehicles (EVs), renewable energy conversion, and data centers highlights the need for efficient and reliable thermal management technologies. In this work, we present a simulation-based study of a 1200 V SiC MOSFET wafer-level power package that integrates chip-package co-design, room-temperature wafer bonding, and embedded microfluidic cooling. By utilizing a room-temperature bonding process to mitigate fabrication-induced warpage and optimizing the chip geometry to balance thermal spreading with mechanical stress, this proposed architecture ensures structural integrity while maximizing heat transfer efficiency. Thermal-fluid-mechanical multiphysics modeling results revealed that the proposed wafer-level microfluidic package achieved a 35.14% reduction in total thermal resistance compared with conventional SiC MOSFET power modules. The design demonstrates improvements in junction temperature uniformity and overall heat dissipation efficiency, which is promising for next-generation high-power density applications.

Two-dimensional materials (2DMs)-based devices exhibit aerospace potential due to their superior properties. However, the operational reliability of 2DMs-based devices in space environments is significantly influenced by charged-particle radiation, necessitating rigorous ground-based radiation tolerance assessments. Current research on radiation effects in 2DMs is primarily experimental, yet such methodologies are inherently time-consuming, resource-intensive, and limited in throughput. To address these challenges, computational modeling and simulation techniques are increasingly being integrated with experimental characterization to accelerate materials design and unravel underlying physical mechanisms. This review systematically evaluates the state-of-the-art multiscale computational frameworks for 2DMs research, focusing on recent advancements, technical challenges, and emerging opportunities. A novel integrative approach is proposed, combining density functional theory, molecular dynamics, Monte Carlo, finite element analysis, and machine learning techniques. Particular emphasis is placed on addressing challenges in multiscale modeling, including accurate representation of complex phenomena across spatial and temporal scales under extreme environmental conditions. Conversely, opportunities for enhancing predictive capabilities are highlighted, with implications for expediting materials discovery in electronics, photonics, energy storage, catalysis, and nanomechanical systems. This comprehensive survey provides a strategic roadmap for future research directions in multiscale computational modeling of 2DMs, emphasizing interdisciplinary methodologies that bridge atomistic simulations with macroscale engineering applications. The insights presented herein aim to advance the development of radiation-hardened 2DMs-based devices for next-generation aerospace systems.

In-sensor-memory computing (ISMC) resolves von Neumann bottlenecks via synergistic innovations across multi-dimensional functional materials, hybrid architectures, and algorithm-hardware co-design. This paradigm empowers ultra-low-latency edge applications, paving the way for autonomous systems, bio-integrated healthcare, and decentralized swarm intelligence. Translating ISMC into scalable commercialization necessitates global Industry-Academia-Research collaboration, application-centric benchmarking protocols, and cross-disciplinary ecosystem enablers.

This study is motivated by a conceptual inconsistency in the physical interpretation of eight-chain hyperelastic theory, which arises from the combined effect of two distinct issues: the use of the marginal projection distribution p_z(|r_z|) as a surrogate for the full probability density of end-to-end distance p_r̄(r̄), and the subsequent reliance on a root mean square (RMS) approximation step in the micro–macro averaging of chain stretch. We first revisit this probabilistic mismatch by reformulating the probability density function of freely-jointed chains (FJCs) in terms of the squared end-to-end vector r², thereby restoring consistency on chain-level statistics. Building on this formulation, the micro–macro mapping averaging of chain conformational free energy is constructed directly in terms of r², leading to a one-step mean-field approximation that avoids RMS averaging. The modified probability transformation is examined by Monte Carlo sampling at the microscopic level. To account for interchain interactions, q-mean statistical description of micro tube confinement was incorporated, leading to the appearance of the general invariant I_q=λ₁^q+λ₂^q+λ₃^q. The resulting continuum constitutive model is assessed against multiaxial experimental data for several polymer networks, including vulcanized natural rubber, Entec Enflex S4035A thermoplastic elastomer, Tetra-PEG, and isoprene rubber vulcanizate. Comparisons with three existing hyperelastic strain energy formulations, the extended eight-chain, extended tube models, and the four-parameter ”comprehensive” model, demonstrate comparable phenomenological accuracy of the current model while providing a clearer and more consistent micro–macro physical interpretation of model parameters. A parametric study further illustrates how the dimensionless parameters n and q govern the shape of the macroscopic stress–strain responses. The present formulation provides a consistent theoretical basis within the scope of hyperelasticity and admits potential extensions toward more complex irreversible phenomena.

As 2.5D/3D advanced packaging moves toward higher interconnect density, glass substrates are attractive for their electrical performance and dimensional stability. However, high aspect ratio, fine pitch through-glass vias (TGVs) are prone to thermo-mechanical failures under long-term thermal shock, and microscale reliability assessment is hindered by costly microstructural characterization and the lack of models capturing long-term microstructural evolution and property degradation. Here, fully copper-filled TGV-Cu structures were fabricated on Schott AF32 glass and subjected to thermal-shock cycling from - 40 ℃ to 125 ℃ (0, 700, 1400, and 2100 cycles). Results show a non-monotonic, location-dependent hardness evolution with pronounced heterogeneity, where the mid-via region exhibits stronger late-stage softening by nanoindentation. EBSD shows grain diameter increases from 0.61 μm to 0.91 μm, while GND density decreases by recovery and then re-accumulates preferentially near the via bottom, evidencing competition between grain-growth softening and dislocation hardening. Hybrid Potts-phase field simulations further predict continuous grain growth and stress relaxation (declining von Mises stress) inside the via; including the glass substrate and interface constraints raises the stress level but preserves the relaxation trend. Moreover, the phase field simulations results of equivalent plastic strain (PEEQ) reveal progressively intensified cyclic plastic localization near the top/bottom caps (Cu/glass intersections), providing mechanistic support for cracking localized at the via mouths.

Flexible strain sensors are key components in emerging fields such as soft robotics and wearable electronics, where robust interfacial adhesion, low electrical resistance, and mechanical stability under repeated deformation are essential. Conventional metal films on polymer substrates often exhibit poor reliability due to thermal expansion mismatch and weak interfacial bonding. Therefore, silver sintering has emerged as a promising low-temperature interconnect material with high conductivity and flexibility, yet controlling porosity during sintering and ensuring strong adhesion to polymers remain critical challenges. This work presents a strategy to expand sintered silver for flexible strain sensor applications. On the one hand, the silver (Ag) porosity was controlled from 20% to 40% through a pressureless sintering process; On the other hand, the Ag-polymer interface was reinforced via a nanoscale surface modulation method using a two-step etching treatments, while the 40 min etched sample demonstrated the most uniform performance, with the lowest electrical conductivity average degradation of 2.97%. The proposed approach effectively improves the microstructural integrity and interfacial reliability, paving the way for high-performance, durable, flexible sensors suitable for the next-generation soft electronic systems.

Solder joint reliability related to failures due to thermomechanical loading is a critically important yet physically complex engineering problem. As a result, simulated behavior is oftentimes computationally expensive. In an increasingly data-driven world, it is popular to use efficient data-driven design schemes. Among the family of efficient optimization methods, Bayesian optimization with Gaussian process regression is a key representative. The authors argue that additional computational savings can be obtained from exploiting thorough surrogate modeling and selecting a design candidate based on multiple acquisition functions. This is feasible due to the relatively low computational cost, compared to the expensive simulation objective. This paper presents a novel heuristic framework for performing Bayesian optimization with adaptive hyperparameters across multiple optimization iterations. A comparative study shows the ability of adaptive Bayesian optimization to save on expensive objective evaluations with respect to the worst-performing regular Bayesian optimization scheme. As an engineering use case, the solder joint reliability problem is tackled by minimizing the accumulated non-linear creep strain under a cyclic thermal load. Results show that adaptive Bayesian optimization can at least match the performance of regular Bayesian optimization in terms of raw objective performance, but achieves this with half of the computational expense budget. This practical result underlines the methodological potential of the novel adaptive Bayesian data-driven methodology to achieve more efficient results and significantly cut optimization-related expenses. Lastly, to promote the reproducibility of the results, the data-driven implementations are made available on an open-source basis.

Higher switching speeds and power densities enabled by SiC MOSFETs make accurate, time-resolved junction-Temperature (Tj) estimation under realistic switching conditions increasingly important for performance validation, thermal design, and reliability assessment. This work presents an electro-Thermal co-simulation framework for SiC MOSFET double-pulse testing, combining measured switching waveforms with a compact thermal network (Cauer model) to translate transient switching-loss energy into (Tj) evolution. A Peak-Power-Threshold (PPT) method is introduced to robustly identify the switching interval and compute instantaneous power and switching energies (Eon, Eoff) from experimental data, enabling consistent comparison between measurement and simulation across few operating points. The proposed approach links dynamic loss extraction to junction-Temperature estimation in a unified workflow, supporting more reliable interpretation of double-pulse tests and improving confidence in electro-Thermal estimation for high-performance SiC power converters.

Due to the better performance of the Wide Band Gap (WBG) devices, there has been a paradigm shift toward WBG-based power modules for diverse applications like Electric Vehicles (EVs). However, the high parasitic inductance value of power modules hinders these devices from unlocking their full potential. Therefore, this paper comprehensively reviews SiC-based Single Side Cooling (SSC) power modules that benefit from low parasitic inductance. The paper also discusses the need to develop newer power modules using modern packaging methods. The surveyed power modules are categorized into three main groups, namely wire bonding, hybrid, and 3D packaging methods. This classification contains several vital parameters of the studied power modules, such as nominal and Double Pulse Tests (DPT) power ratings, parasitic inductance, size, etc. The main features and characteristics corresponding to the reviewed power modules' packaging methods and techniques are also briefly described. Finally, a thorough discussion about challenges and future trends is highlighted before concluding the paper.

The evolution in modern-day electronics extends beyond miniaturization and significantly increases the number of power cycles operating with low temperature swings (few tens of degrees). This shift has raised the reliability requirements and expectations for electronics systems to last longer. The transition of vehicles and autonomous systems to zonal architectures, driven by electrification, has increased power-on hours of electronic components, leading to higher system complexity and more challenging reliability testing. Currently, standardized stress- based tests and predictive models have constraints, as they are designed for large temperature swings and are inaccurate for evaluating low T conditions. Additionally, the damage caused by Δ millions of low thermal swings is less explored in simulation and qualification, making it important to analyze the influence of low ΔT on solder joints to understand field reliability risks.An Arduino-based testing setup was developed to apply active thermal cycles to WLCSP40 using relay-controlled switching with 6-second cycles. Samples were tested from 50k to 800k cycles to analyze the long-term damage accumulation under such conditions. In addition to experiments, a thermomechanical simulation in COMSOL incorporated ΔT values obtained from thermal camera characterization to establish realistic boundary conditions. It helps to correlate experimental results and identify critical locations within the solder joints where damage is most likely to occur.Cumulative plastic strain analysis and cross-sectional imaging revealed cracks in the solder bulk at 40 °C, demonstrating microstructural fatigue under low ΔT cycling. Furthermore, based on strain per cycle values, the results exhibit how the active and passive thermal cycles differ.