Control theory underpins the stabilization of dynamic systems, including cardiac tissue, where disruptions in electrical conduction cause arrhythmias. Current treatments either act rapidly but without precision or deliver targeted interventions that cannot adapt in real time. We present an integrated platform combining optical voltage mapping (OVM), machine learning (ML), and optogenetics for autonomous, real-time detection and correction of cardiac rhythm disorders in vitro. OVM provides high-resolution membrane potential visualization; the ML module identifies arrhythmic events and drives microLED-based light patterns restoring normal conduction; and optogenetics enables light-based modulation of excitable cells. This integration of electrical, optical, and bioelectrical domains through a unified computational control layer enables adaptive, closed-loop rhythm stabilization, a significant advance in real-time electrophysiological interventions. Because inference and actuation run in real time on modest hardware, the same control loop could be embedded into miniaturized devices or microcontrollers, accelerating the transition from in-vitro to in-vivo automated rhythm management.

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.

The reliability of through-glass via (TGV) interconnects is critical for advanced semiconductor packaging. This work investigates microstructural and mechanical evolution in electroplated TGV–Cu subjected to long-term aging at 250 °C. TGV samples were fabricated via laser-induced etching and double-sided copper electroplating, then aged for up to 1008 h. Nanoindentation revealed region-dependent reductions in hardness (from 2.0–2.5 GPa to below 0.5 GPa) and modulus (from 110–130 GPa to 40–90 GPa), with surface-near regions most affected. The glass substrate maintained stable mechanical properties until microcracks formed after 1008 h. EBSD quantification showed grain-size enlargement from 0.46 µm to 1.86 µm and a concurrent decrease in dislocation density. Molecular dynamics simulations of 3, 4, 5 nm grains corroborated the inverse relationship between grain size and micro-mechanical properties. A hybrid Potts-phase field model further linked grain coarsening to stress relaxation and elastic-energy minimization, revealing that as grains grow, the overall von Mises stress in the structure decreases; high-modulus grains retain relatively higher local stresses, while low-modulus, low-stress grains exhibit faster growth rates. Electrical I–V measurements confirmed stable ohmic behavior, despite a drop in insulation resistance. These integrated experimental and computational insights provide theoretical guidance for optimizing TGV interposer design and ensuring long-term operational reliability in heterogeneous integration technologies. (Figure presented.)

This work proposes a one-stage bidirectional rectifier with a pre-charge voltage (VPC) based perturb-and-observe (P&O) maximum power point tracking (MPPT) algorithm for triboelectric energy harvesting. The proposed VPC-based MPPT dynamically locates the true MPP while accounting for circuit non-idealities, using a simplified implementation compared to conventional P&O methods. Consequently, it achieves 93% MPPT efficiency and 8.86× energy enhancement compared to the full-bridge rectifier.

Ultrasensitive and specific detection of low-abundance tumor biomarkers remains a major challenge for early and minimally invasive cancer diagnosis. Here, we present a high-performance biosensing platform that integrates a genetically engineered bacterial S-layer with an AlGaN/GaN high-electron-mobility transistor (HEMT) sensor for label-free detection of tumor antigens. As a proof-of-concept, the ovarian cancer antigen human epididymis protein 4 (HE4) was selected . Specifically, the S-layer protein rSbpA was fused with HE4-specific nanobody 1G8 to construct a bifunctional membrane capable of self-assembling into an ordered biorecognition layer on the sensor surface. Compared to conventional chemical crosslinking, S-layer-driven assembly increased antibody loading by 50 % and minimized nonspecific adsorption in plasma environments. The resulting HEMT sensor detected HE4 across a dynamic linear range (10⁻²¹ to 10⁻¹⁴ M), identifying patients with ovarian cancer with 100 % diagnostic accuracy (AUC = 1.0). This study establishes a versatile and modular biosensing strategy for ultra-low-abundance biomarker detection with broad potential applications in the precision diagnostics of cancer and other diseases.

Sintered Cu nanoparticles (Cu NPs) are promising interconnection materials for high-temperature power electronics, yet how their authentic three-dimensional pore architecture governs microscale deformation remains unclear. Here, synchrotron nano-computed tomography (nano-CT) was combined with in-situ micropillar compression, explicit dynamic elastoplastic finite element analysis, and TEM/TKD characterization to interrogate sintered Cu NPs. The nano-CT voxel size was 45 nm, and the reconstructed volume corresponded to a cylinder 16 µm in diameter and 10 µm in height. The average sectional porosity was 12.44%, with a systematic discrepancy between two-dimensional and three-dimensional porosity quantification. During loading, the porosity decreased to 9.55% while the pore aspect ratio increased from 1.82–2.35. Finite element analysis further showed pronounced pore-adjacent stress/strain localization at the elastic–plastic transition, with local stress and equivalent plastic strain reaching 650 MPa and 1.7 × 10⁻², compared with 250 MPa and 1.1 × 10⁻³ in adjacent regions. The GND density increased by 95.9% at a compressive strain of 26%, linking pore-induced strain gradients to dislocation accumulation. These results quantitatively connect authentic three-dimensional pore architecture, local deformation localization, and dislocation-mediated strengthening in sintered Cu NPs. Highlights Synchrotron nano-CT (45 nm voxel size) reconstructed a 16 × 10 µm cylindrical volume of sintered Cu NPs and resolved the authentic 3D pore network. Sectional porosity was 12.44%, and 2D/3D quantification showed a systematic discrepancy, with porosity decreasing to 9.55% and pore aspect ratio increasing from 1.82 to 2.35 during compression. Pore-adjacent localization was quantified at the elastic–plastic transition, with local stress/PEEQ reaching 650 MPa and 1.7 × 10⁻² versus 250 MPa and 1.1 × 10⁻³ in adjacent regions. A 95.9% increase in GND density at 26% compressive strain links pore-induced strain gradients to dislocation accumulation and strain-gradient-driven strengthening.

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.

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.

This paper investigates the surge reliability of commercial 1200V SiC MOSFETs through a combined approach of experimental testing and multiphysics simulation, elucidating the failure mechanisms under both step and repetitive surge current stress. The innovative integration of package-level electro-thermal coupling simulation with transient junction temperature estimation overcomes the limitations of conventional methodologies that rely solely on decapsulation analysis and Technology Computer Aided Design (TCAD) simulation. Experimental evaluations on six Devices Under Test (DUTs) with distinct structural configurations, employing surge testing and failure analysis techniques including C-mode Scanning Acoustic Microscopy (C-SAM), optical microscopy, and Scanning Electron Microscope (SEM), confirm that device failure primarily originates from gate-source short circuits caused by aluminum bonding wire melting. COMSOL multiphysics simulations further replicate the transient thermal characteristics of bonding wire regions, demonstrating rapid temperature escalation to the Aluminum melting point within 5-6 ms during surge events. A transient thermal resistance-based junction temperature characterization method is proposed, revealing an inverse proportionality between thermal resistance and chip area.

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.

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.

Field-Stop Insulated Gate Bipolar Transistors (FS-IGBTs) are widely used in various power applications due to their low conduction and switching losses. However, further reductions in cell pitch lead to increased cell density, resulting in higher saturation current that adversely impacts the short-circuit ruggedness essential for applications such as welding machines and motor drives. This paper details the design and fabrication of a 650V, 75A FS-IGBT. By incorporating dummy gate and emitter trench structures within the active gate and optimizing the layout of the three cell structures, the short-circuit characteristics of the device are markedly improved. Experimental tests confirm that the device exhibits both low saturation on-state voltage and short-circuit ruggedness. This study further investigates the circuit parameters related to short-circuit conditions and comprehensively analyzes the impact of each parameter on the short-circuit characteristics of the FS-IGBT. The experimental results indicate that the bus voltage VDC, gate voltage VG, and temperature TC significantly influence the short-circuit performance of the FS-IGBT. Therefore, a moderate decrease in VDC, VG, and TC can effectively enhance the short-circuit ruggedness and the short-circuit withstand time tSC of the device.

Although silver sintering is widely used in die attach, its reliability under low-pressure and pressureless sintering conditions remains a challenge, and the degradation mechanism needs to be addressed urgently. This study investigates the degradation mechanisms of silver sintered die attach subjected to thermal shock (TS) (−45 °C to 125 °C), revealing its microstructural evolution, degradation of mechanical properties, and pore dynamics. After 1500 cycles, the average porosity did not decrease and remained at approximately 10%, but the porosity distribution exhibited significant heterogeneity, forming three characteristic regions of high porosity, low porosity, and crack regions. With the porosity evolution, the grain size increased by a factor of 2.1–2.8, with the largest grain sizes in the crack region, and the recrystallization fraction decreased significantly, from 89.6 % to a range of 37.3 %–72.1 %. Additionally, the combination with the decrease in both statistically stored dislocation density and total dislocation density reveals a decline in hardness and yield strength of silver sintered layer from 1.05 GPa and 326 MPa to 0.92 GPa and 231 MPa, respectively, gradually diminishing their ability to impede pore migration and merging. Thermal-mechanical coupling simulations based on the actual porous structure images show that the mismatch in the coefficient of thermal expansion induces alternating tensile and compressive thermal stresses, driving pore evolution and crack propagation. The kinetic Monte Carlo (KMC) Potts model based on Kawasaki dynamics combined with pore conservation can effectively predict the long-term pore evolution in this case. These findings provide important guidance for optimizing the sintering process and improving the application of silver sintering materials in high-reliability electronic packaging.

Ion implantation and subsequent annealing reshape the defect landscape and stress state of compound semiconductors, yet the temperature-dependent mechanisms in SiC remain incompletely understood. Here, we utilize molecular dynamics (MD) simulations and confocal micro-Raman measurements to resolve how implantation temperature and post-annealing regulate lattice disorder, amorphization kinetics, and residual-stress evolution in chemical vapor deposited (CVD) 4H-SiC. MD reveals surface-nucleated amorphization that propagates inward, whereas elevated implantation temperatures activate defect recombination pathways that suppress amorphous-layer formation. Raman signatures of optical-phonon shifts, linewidth broadening, and amorphization bands track the coupled evolution of lattice disorder and stress. Experimentally, increasing implantation temperature smooths the surface (S_a 0.133 → 0.101 nm) and reduces the amorphous-layer thickness (from ∼700 nm at 25°C to undetectable at 500°C), while driving more compressive residual stress (−57 → −132 MPa). Post-annealing largely restores phonon lifetimes and eliminates amorphization signatures, consistent with the recovery trends predicted by MD. These results delineate a thermal-treatment window that controls amorphization and residual stress in 4H-SiC, providing a transferable Raman-based methodology for nondestructive assessment of implantation-induced damage in compound semiconductors.

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.

In this work, the Material Point Method (MPM) is reviewed for application in the microelectronics industry. Microelectronic processes often involve large deformations, evolving interfaces, multiphysics coupling, and complex geometries that challenge conventional mesh-based methods such as the finite element method (FEM). Meshless methods provide an alternative solution that avoids these issues. A comparison is made between Smoothed Particle Hydrodynamics (SPH), Element Free Galerkin (EFG), peridynamics, Radial Basis Function–Finite Difference (RBF-FD), and MPM, evaluated with respect to convergence, consistency and stability, boundary enforcement, adaptivity, coupling, and industrial applicability. Based on this assessment, MPM and its main variants (BSMPM, GIMP, CPDI, and TLMPM) are examined in depth. The method’s ability to address large deformations, moving interfaces, contact, history-dependent material behavior, and multiphysics interactions is examined. The underfill process is used as a representative use case to illustrate challenges such as free surface flow, void formation, thermomechanical coupling, and residual stress. Overall, MPM shows strong potential, although further benchmarking and validation are required for widespread industrial adoption.

Finite element (FE) simulations of structures and materials are becoming increasingly accurate, but also more computationally expensive as a collateral result. This development occurs in parallel with a growing demand for data-driven design. To reconcile the two, a robust and data-efficient optimization method called Bayesian optimization (BO) has been previously established as a technique to optimize expensive objective functions. The mesh width of an FE model can be exploited to evaluate an objective at a lower or higher fidelity (cost & accuracy) level, which is the domain of multi-fidelity BO (MFBO) applications. However, BO and MFBO are usually not directly compared in the literature. Moreover, sampling quality and assessing design parameter sensitivity are often underrepresented parts of data-driven design. This paper combines global sensitivity analysis and (MF) BO into a novel, efficient Bayesian data-driven framework. We compare the performance of BO with that of MFBO by maximizing the energy absorption (EA) problem of spinodoid cellular structures. The findings show that similar or better designs are suggested by MFBO with 16% fewer expensive objective evaluations compared to BO when maximizing the EA. The results, which are made open-source, serve to support the utility of multi-fidelity techniques across expensive data-driven design problems.

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.

While silver-based sintered materials are limited by cost and electromigration, and copper faces challenges with oxidation at high temperatures, Cu-based composite sintering materials offer promising alternative solutions. This review examines recent advances in Cu-based composite sintered materials for die-attach in power electronics packaging, focusing on their mechanical, thermal, electrical properties, and reliability. This review systematically categorizes such compounding strategies, including direct mixing, core-shell structures, and alloying, analyzing the impact on composite properties. Furthermore, the reliability of Cu-based composite sintered joints is evaluated, addressing high-temperature storage, thermal cycling, corrosion, and electrochemical migration. Challenges such as oxidation resistance, process optimization, and cost-effectiveness are discussed, together with future research directions. This work aims to support researchers in advancing Cu-based composite sintering materials research and development, broadening material options for high-temperature power electronics packaging applications.

Reducing parasitic parameters and thermal resistance is critical for advancing power electronic devices. This article designs and evaluates the three printed circuit board (PCB) embedded 1200 V SiC mosfet half-bridge packaging cells, where the traditional wire bonding process is replaced by a redistribution layer (RDL) technique. A comprehensive evaluation of their electrical performance, thermal management, and mechanical performance is conducted. The three solutions that employ panel, active metal brazing (AMB), and lead—frame carriers, are developed through a streamlined process that includes die attach, molding, drilling, plating, and etching. This packaging approach readily reduces the parasitic inductance to below 5 nH. By utilizing a single-layer RDL with mutual inductance cancellation, the power loop inductance is reduced to as low as 2.4 nH (at 10 MHz), and the gate loop inductance to 1.57 nH (at 10 MHz). The junction-to-case thermal resistances of the three solutions are 1.88, 1.03, and 0.73 K/W, respectively. Compared with the other two packaging cells, the cell selecting AMB as a carrier reduces SiC mosfet operational stress and deformation by approximately 34% and 75%. The lead—frame carrier offers superior thermal dissipation for potential TO package replacement in half-bridge topologies, while the panel solution is promising for dual-sided cooling applications. With low thermal resistance, minimal stress, and excellent backside electrical insulation, the packaging cell with an AMB carrier is ideally suited for integration with heatsinks in traction inverters.