Silicon-Carbide (SiC) MOSFETs are widely used in high-power and high-efficiency applications such as electric vehicles and power supplies. However, long-term reliability remains a critical concern, particularly under extreme operating conditions. This work aims to explain the health monitoring of SiC metal oxide-semiconductor field effect transistors (MOSFETs) through precise junction temperature (T_j) profiling based on performed measurements. The study focuses on the temperature-dependent behavior of the on-resistance (RDS(on)), a key parameter that varies with the aging, degradation, and temperature of the device. By systematically measuring R_DS(on) at different temperatures and at various stages of the operating life of the device, we can establish a predictive model to assess the health of SiC MOSFETs. The importance of pulse duration of the drain current is stressed to avoid the self-heating effect with some device physics insights. The proposed methodology enables better understanding of the SiC MOSFET performance for future real-time condition monitoring, facilitating early failure detection and lifetime estimation. This approach provides valuable information for improving reliability and optimizing maintenance strategies in power electronics systems. Experimental results validate the effectiveness of the proposed method and give direction for future research opportunities.

The heat produced within the device in its package depends on the power supplied to each IP block. The improper placement of an IP block with high power consumption can become a reliability risk for IC packages, as it can significantly affect the reliability of solder balls due to thermal, mechanical, and electrical factors. It also mitigates the effect of thermal cycling, because it depends upon heat management within the package. Improper IP block placement can block heat flow paths, which results in poor thermal dissipation and higher temperature which can create a higher strain on the solder balls affecting their lifetime. Therefore, it is crucial to understand the interplay between the correct placement of IP blocks and solder balls to enhance solder ball reliability. COMSOL-based simulation study will be done on a WLCSP (Wafer Level Chip Scale Package) mounted on a PCB board on which solder balls are connected to copper layers on the die and PCB sides. Variations in the placement of the IP blocks. Thermal cycling and the variations in the placement of the IP block within the package will be taken as a loading condition. Therefore, this influences the susceptibility of device failure due to solder ball fatigue. Based on board-level passive cycling, the solder balls which are located under the IP blocks are the most likely ones affected by this stress. The results will be analyzed to optimize the layout of the IP blocks, concerning accumulated plastic strain on the solder balls at different locations of the IP block placement. It can help to understand the influence of the position of the IP block, reduce stress concentration, and minimize thermal cycling effects, which leads to an increase in the reliability of the solders joint and IC package itself.

A meshless method is used to simulate the Fluid-Structure Interaction (FSI) in a micropump intended to treat nerve injury. Conventional meshbased methods can suffer from mesh deformation and quality issues, and find it difficult to track the fluid-structure interface. The Material Point Method (MPM) combines Lagrangian material points with an Eulerian computational grid, thereby avoiding any mesh related problems. To simulate the valve dynamics in the micropump, MPM was used to analyze the effect of the valve length on the behaviour of the pump. A longer valve length takes longer to open, as it sticks to the valve seat, meaning the pump needs to generate more pressure to open the valve. This contribution shows that MPM simulations can be used to optimize the valve design for implantable micropumps.

This paper investigates the effects of three ageing factors (chemical, humidity, and temperature) and their interactions on the physical properties and degradation of silicone sealant used in microelectronic applications. The thermal degradation of silicone sealants was investigated by exposing samples to temperatures in the range of 150 up to 175 °C. Also, a set of samples were aged at 40 °C in a salt spray set-up with 100 % humidity in a salty atmosphere. Results showed detectable changes in the FTIR spectra of aged specimen as compared with the as-received sample. In all accelerated testing conditions, peak intensities decreased with ageing time, inferring that that the surface characteristics of the sealant is affected by ageing. Shear test results showed that with increasing the ageing time, the maximum shear stress in most cases has decreased in all ageing conditions. Also, it appears that samples with longer ageing times have experienced more elongation before failure. Results also show that salt spraying of specimens is associated with a decrease in the mechanical properties of the sealant, indicating the deleterious implications of ionic contaminations for the mechanical properties of samples.

In this study, we introduced a hybrid Potts-phase field model to simulate the co-evolution of grain growth and pores migration in sintered silver layers. The Potts model is good at capture the grain growth dynamics, while the phase field model describes the evolution of the porous network. These models are coupled via a hybrid free energy function to achieve a realistic representation of the microstructure evolution. This study further extends the hybrid model by incorporating (a) a flexible exchange interaction matrix to model the crystal anisotropy in grain growth, (b) Glauber or Kawasaki dynamics to describe different diffusion mechanisms, and (c) the effect of pinning sites, representing impurity-driven grain boundary stabilization. The computational framework is implemented using Taichi Lang, which allows for efficient parallel simulations. Results show that the model effectively captures the long-term evolution of the sintered silver microstructure in good agreement with experimental observations. This hybrid model is a powerful tool to predict microstructural reliability of sintered silver die attach layers, supporting material design and process optimization for high-power electronic applications.

Prognostic monitoring of power quad flat no-lead (PQFN) packages with four distinct silver pastes, each varying in material composition (pure-Ag and resin-reinforced hybridAg) and sintering processes (pressure-assisted and pressureless), was investigated in this study. The PQFN packages with silver sintered die-attach materials were subjected to thermal cycling tests (?55 ° C to 150 ° C), and the performance degradation was evaluated based on the following metrics: 1) electrical ON-state resistance RDSon monitored periodically at specific thermal cycling intervals and 2) transient thermal impedance Zth(t = 0.1 s) monitored online during thermal cycling. These measurements were further validated using acoustic microscopy imaging and cross-sectional inspection. The pressureless Ag-sintering material demonstrated comparable performance to pressure-assisted Agsintering, with a dense microstructure, and consistent electrical and stable thermal performance. Whereas the pressureless resinreinforced hybrid-Ag material exhibited degradation with a relative increase of 33% in RDSon, 38% in Zth(t = 0.1 s), and 67% delamination of the die-attach interface over 1000 cycles. These findings suggest that pressureless Ag-sintering may offer a viable alternative to pressure-assisted methods for lead (Pb)- free die-attachments, while resin-reinforced hybrid-Ag requires further development for improved thermomechanical reliability..

The increasing awareness of environmental concerns and sustainability underlines the importance of energy-efficient systems, renewable energy technologies, electric vehicles, and smart grids. Hence, stringent constraints and safety regulations have been prompted to meet reliability standards in power electronics. This chapter provides a comprehensive outlook on the current state of power semiconductor devices, field-critical applications, dominant degradation mechanism (chip-related and package-related), and the emerging measurement techniques for reliability/condition monitoring. This chapter delves into the underlying physics behind each reliability measurement method reviewed. A comparative summary of cost, complexity, online monitoring capability, accuracy, and intrusiveness is provided to enable readers to make informed decisions about the measurement methods. This chapter emphasizes the significance of early fault detection through online monitoring, as it can effectively reduce system downtime for seamless non-interruptive operation.

Board level reliability can be of high interest for automotive electronic components when exposed to vibration-prone environments. However, the absence of an industry standard for board level vibration testing poses several challenges in establishing a well-characterized test setup. One of the challenges is that automotive applications can induce abnormal stresses on components that can lead to early failures in the field. Such loading conditions are not always covered in the current board level vibration test methods. This paper aims to correlate the stresses from automotive modules to board levels by measuring the printed circuit board (PCB) vibration spectrum. Firstly, the study compares and assesses several module board level vibration measurement units, such as LASER Doppler Vibrometer (LDV), strain gauges, and accelerometers. Experiments and simulations show that LDV enables good correlation with Micro-electro Mechanical Systems (MEMS) accelerometers. Secondly, the module-board interaction unveils insights into several module design features that impact the PCB vibration response and solder joint interconnect reliability. These findings underscore the necessity for the user to correctly validate the reliability of packages beyond board level testing, i.e., at the module level. This reliability test approach enables the translation of reliability test results from the lab to the field life of components once built in the final application equipment.

This study introduces a training protocol utilizing Convolutional Neural Networks (CNNs) and Confocal Scanning Acoustic Microscopy (CSAM) imaging techniques to classify Power Quad Flat No-leads (PQFN) package delamination. The investigation involves empty PQFN packages with varied substrate metallizations subjected to thermal cycling. Four delamination classes were labeled: Die-pad delamination (Class-A), Bond-pad delamination (Class-B), both Die-pad and Bond-pad delamination (Class-C), and No delamination (Class-D). Due to data imbalance, additional randomness was introduced for distribution balancing. Residual Networks (ResNet-18) based CNN model was selected for classification. Five-fold cross-validation assessed overfitting performance concerning input data size, image resolution, and batch size. The ResNet-18 prediction performance was evaluated using precision and recall metrics, with the model achieving average precision and recall scores of 0.86/1 and 0.83/1, respectively. Additionally, a comparison of delamination among different substrate metallizations was presented with Ag and NiPdAu indicating significant delamination compared to bare Cu substrate. This study pioneers the integration of CNNs with CSAM imaging for package defect detection and classification, laying the groundwork for future research to address the complex interplay of multiple failure mechanisms in functional packages.

The various application scenarios of triboelectric nanogenerator (TENG) have attracted increasing research interest, while one of the biggest challenges is the energy extraction efficiency. Due to the small and time-varying inherent capacitor in a TENG, the previous energy extraction techniques e.g., full-bridge rectifier (FBR) and bias-flip (BF) rectifier, performed not well. To extract more energy from TENG, this article proposed a fully integrated switched-capacitor (SC) rectifier with an electrostatic charge boosting (ECB) technique, achieving simultaneous extraction from the synchronized triboelectric energy and self-excited electrostatic energy. The proposed rectifier was fabricated in a 180-nm BCD process. With the proposed ECB technique, the theoretical analysis and measurements show a quadratically increasing output power with respect to the rectification voltage, attaining a constant maximum power point (MPP) at the breakdown voltage of the circuit. A maximum output power of 127.6 μ W is measured with a TENG fabricated in-house. Compared to a passive FBR, the proposed rectifier enhances the output power by 14 times.

The demand for rapid advancement in AI, mobile and automotive markets is pushing the boundaries of electronic packaging, including heterogeneous integration, high-power packages, and large-die packaging. Against this backdrop, machine learning technologies emerge as dynamic tools for correlation building and classification, revolutionizing the traditional approaches to design, manufacturing, and testing in electronic packaging, as well as the Design for Reliability (DfR) methodologies.This paper reviews the most recent AI-assisted approach for electronic packaging and then focuses on the AI-assisted DfR (AI-DfR) approaches. Our examination reveals that AI methods have been adapted to meet the specific needs of electronic packaging. The industry’s anticipation for AI-DfR stems from its potential to address prevailing reliability design challenges, yet its multidisciplinary essence poses hurdles to swift progress. This review proposes future directions for AI-DfR’s development, spotlighting critical areas such as the quality and efficiency of finite element modeling, design and optimization of training models, selection of AI models, and maintenance and value enhancement strategies.

A triboelectric nanogenerator (TENG) is a kinetic energy transducer with small and time-varying internal capacitance, which increases the difficulties of extracting harvested energy. In this paper, an efficient rectifier, hybridizing synchronized electric charge extraction (SECE) and bias-flipping techniques, is proposed. The two techniques alternatively operate at opposite voltage polarities of the TENG. By taking advantage of the varying capacitance, the proposed synchronized extraction and flipping (SEF) rectifier shows significantly improved energy extraction performance. The design is implemented in a 180-nm high-voltage BCD technology, and the results show a 7.4X energy extraction enhancement, 65-V voltage tolerance, and 35-nA quiescent current.

A significant challenge in the implementation of health monitoring systems for estimating the health state of devices is the lack of accurate information about design details. This challenge is particularly prominent in the field of power electronics, where both IC designers and converter designers are often hesitant to share information about their designs. Addressing this issue, this paper introduces a novel AI-driven digital twin modeling methodology that enables the detection and classification of failures in power semiconductors, particularly Wide Band Gap semiconductors. By employing AI-based system identification techniques, this method offers a noninvasive approach to health monitoring of power switches with high resolution, even while operating under real conditions. The proposed method has been validated by simulating wire bond failure in a SiC power MOSFET using MATLAB SIMULINK, and the results demonstrate its accuracy.

Solder fatigue is a key failure mode in the electronic industry. Monitoring the actual degradation of the solder under real-time conditions in any application would be extremely beneficial. In this chapter, we describe the combination of experimental material characterization with numerical finite element (FE) simulations to obtain a prognostics and health monitoring (PHM) methodology for LED drivers used in outdoor lighting applications. Experimental characterization of a new type of solder is described. A FE model is created of a typical component in electronic drivers. The calculated damage level and the collected life data correlate together and form a model for predicting the lifetime of the drivers at certain user condition. The developed PHM methodology helps in identifying and reporting the failure of the driver in real time or can be used for predicting the actual remaining useful life (RUL).

This study explores a novel approach to monitor the spectral emission of LEDs by estimating the spectral power distribution from the spectral sensor responses during an accelerated aging experiment. Two methods for reconstructing the actual LED spectra from sensor responses are presented and tested, one solely requires sensor datasheet information and the other uses a full spectral characterization of the sensor's spectral sensitivities. The reconstruction results show that a spectral sensor can provide accurate spectral estimates even after severe LED degradation. Only for an LED that suffered a phosphor crack, affecting its spatial radiation characteristics, limited ability to estimate the true spectral power distribution without prior assumptions about the spectral changes must be reported. Overall, the use of a spectral sensor, even without detailed characterization of the sensor itself, allows for an accurate monitoring of the true emission of LEDs, with a maximum radiometric error of 0.73 %, a maximum colormetric error of 0.0017Δ u'v' and a maximum spectral nRMSE error of 0.0097 compared to a spectroradiometric measurement. This advance holds great promise for improving lighting technology, particularly in applications that require constant radiometric output and stable color.

Due to the deficient passivation of the interface between silicon carbide and silicon dioxide, the defect-induced capture and release of trapped charges triggered by external Bias Temperature Stress (BTS) leads to parameter shifts and degraded device performance. This study models the trap-induced transient current in silicon carbide metal-oxide-semiconductor capacitors, providing insight into how capacitance and conductance change during C-V measurements under conditions of high temperature, varied frequency, and varied applied voltage.

Resin-reinforced Ag sintering materials represent a promising solution for die-attach applications in high-power devices requiring enhanced reliability and heat dissipation. However, the presence of resin and intricate microstructure poses challenges to its thermal performance, and improvement strategies remain unclear. This work utilizes 3D FIB-SEM nanotomography to reconstruct the microstructure of this material under various process conditions. The analysis reveals that, even with an Ag volume fraction as low as 47.3%, Ag particles form a robust 3D network. Geometric tortuosity quantifies the effect of different sintering conditions on the Ag particle network in all spatial directions. Effective thermal conductivity is simulated based on realistic microstructure models. Results show a significant negative correlation between tortuosity and effective thermal conductivity. Increasing sintering temperature in Model B notably reduces tortuosity and enhances effective thermal conductivity. Sensitivity analysis underscores the dominant role of Ag volume fraction in regulating effective thermal conductivity. Finally, transient thermal impedance measurement of this material as a thin die-attach layer in actual high-power devices demonstrated its application potential. This article strives to explore the relationship between process, microstructure, and thermal properties of this material to provide a reference for further development.

The introduction of silicon carbide(SiC) has reduced the superiority of traditional silicon-based power module pack-aging strategies. As packaging strategies become increasingly complex, classical thermal modelling tools often prove inadequate in balancing efficiency with accuracy. Integrating these tools with machine learning (ML) can significantly enhance their application potential. This discussion commences by addressing the pressing issues in thermal modelling of SiC modules, specifically the challenges associated with multiple heat sources and heat spreading. During the design stage, ML models can swiftly simulate the thermal response of various packaging strategies, aiding engineers in eliminating ineffective options. In the monitoring phase, the employment of a digital twin enables a deeper investigation into degradation phenomena. This article reviews the current status and explores the potential applications of ML in thermal modelling of SiC power modules.

The ability to accurately predict the reliability and lifetime of electronics is of great importance to the industry. The failure of the solder joint is of particular interest for these predictions, because of their susceptibility to failure under thermo-mechanical stress. However, the experimental or even conventional simulation techniques employed to estimate the lifetime of a solder joint are often too expensive or time consuming to be of practical use. Therefore, this work introduces a physics-informed Long Short-Term Memory (LSTM) to predict the plastic strain in the critical area of the solder joint. The predicted values are in agreement with the values gained from finite elements, thereby demonstrating the advantage of applying the proposed methodology.

In this article, we provide a comprehensive review of defect formation at the atomic level in interfaces and gate oxides, focusing on two primary defect types: interface traps and oxide traps. We summarize the current theoretical models and experimental observations related to these intrinsic defects, as they critically impact device performance and reliability. By integrating theoretical insights with experimental data, this review provides a thorough understanding of the atomic-scale interactions that govern defect formation.