The modeling of spectral characteristics of light-emitting diodes (LED) has been addressed in various studies. We extend the current state of knowledge by modeling the spectral characteristics of commercially available high-power LEDs, exhibiting a temperature-dependent degradation, by using a different modeling strategy. To this end, the state of the art approach of an additive superposition of probability density functions (PDF) is compared with an unsupervised machine learning approach called non-negative matrix factorization (NMF). The stress test data used in our modeling routine was collected for a period of 6000 hours at four different case temperatures between 55 C and 120 C. The results of the accelerated stress tests indicate a temperature-activated aging process, which can be described using the Arrhenius equation. By combining the Arrhenius equation with the modeling parameters, the spectral characteristics can be modeled for 6000 hours of stress at four different stress test temperatures. The introduced spectral modeling approach using non-negative matrix factorization achieves CIE 1976 UCS chromaticity differences primarily smaller than Δ u'v'≤ 0.001 and proves to be superior to superimposed probability density functions in terms of colorimetric reconstruction accuracy, modeling complexity and robustness against spectral outliers.

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.

Triboelectric nanogenerator (TENG), advantageous in high energy density and flexibility, is promising as a sustainable energy source but can hardly be used to power edge devices directly due to its high-voltage AC output and varying capacitive impedance. To address it, this work proposes a power-conditioning interface with a fully integrated dual synchronous switch harvesting on capacitors (D-SSHC) rectifier for triboelectric energy extraction. Furthermore, a full digital duty-cycle-based (DCB) maximum power point tracking (MPPT) algorithm is developed to optimize the energy harvesting efficiency with simple implementation and continuous tracking. Designed and fabricated in a 0.18-μm BCD process, the proposed interface can extract energy at a maximum output voltage of 70 V. According to the measurement results, it achieves 99% MPPT efficiency and an energy extraction improvement of 598% compared to a full-bridge rectifier.

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.

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 review is aimed at providing an overview of the technologies of currently-available UVC LEDs, on the challenges that these devices have to face, and on the peculiar features that these modern solid-state emitters exhibit. In particular, this paper is aimed at serving as a bridge between device developers and system manufacturers, by increasing awareness of the differences, both in terms of reliability and operation, that AlGaN-based UVC LEDs show with respect to their visible InGaN/GaN-based counterparts. In this view, this work reports performance and lifetime figures of both commercially-available and research-grade LEDs, showing their limitations in terms of temperature- and current-dependency of the emission spectrum. Both catastrophic and gradual processes that lead to device degradation are discussed, with a particular focus on the kinetics that device properties exhibit during prolonged operation. Moreover, also package-related degradation processes are investigated, which stand-out due to the peculiar structures and materials required to sustain both high-energy UV photons and high localized self-heating, while maximizing the optical efficiency of the LEDs. Ultimately, the data reported within this paper should help the final user in predicting and mitigating degradation effects, while also serving as a reference to manufacturers for the improvement of next generation devices.

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 reliability of solder joints plays an increasingly important role in power electronics. The thermal fatigue experienced due to the temperature fluctuations cause catastrophic failures. However, the ability to predict the fatigue for different thermal cycles is lacking. Experimental or simulation based approaches are typically too expensive to be conducted for a wide range of thermal loading conditions. A physics informed Long Short-Term Memory (PI-LSTM) is proposed here for predicting the plastic strain and related fatigue lifetime in solder joints. The LSTM model is trained on data generated by FEM simulations, enhanced by incorporating the flow rule into the loss function. The PI-LSTM accurately predicts the plastic strain and the stress components, enabling efficient reliability predictions. Using different reliability models, the estimated cycles to failure are found to be in close agreement with those from conventional FEM simulations, demonstrating the PI-LSTM's capability for reliability assessments.

This study investigates the interface strength and fracture behavior of sintered copper (Cu) nanoparticles (NPs) for all-Cu integration in advanced microelectronics packaging. Micro-cantilever bending tests on three configurations (Cu NP-notched, interface-notched and un-notched micro-cantilevers) were analyzed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), transmission Kikuchi diffraction (TKD) and cohesive zone model (CZM). The interface-notched micro-cantilevers demonstrate superior fracture resistance, with a stress intensity factor (K_Q) of 2.88±0.10 MPa m^1/2, compared to 2.12±0.11 MPa m^1/2 for Cu NP-notched micro-cantilevers. Simulation results, consistent with experimental results, reveal that Cu NP-notched micro-cantilevers exhibit lower fracture resistance due to porosity and stress concentrations, while interface-notched micro-cantilevers show enhanced strength, attributed to robust bonding and reduced void distribution. Un-notched micro-cantilevers display superior load-bearing capacity, with cracks bypassing the interface and propagating through porous regions. Moreover, in un-notched micro-cantilevers, a synergistic deformation mechanism is observed, where crack propagation through the sintered Cu NPs coexists with plastic slip deformation in the Cu substrate. These findings highlight the strong interfacial bonding and effective stress transfer at the Cu substrate-sintered Cu NP interface, validating the feasibility of direct sintering using Cu NPs without additional coatings.

Sintered Cu nanoparticles (NPs) are promising for high-performance electronics due to their excellent thermal and electrical conductivity, as well as mechanical reliability. This study investigates the microscale mechanical behavior of sintered Cu NPs with a bimodal particle size distribution, focusing on strain rate and temperature effects. Micro-pillar compression tests were performed across strain rates of 0.0001 s⁻¹ to 0.01 s⁻¹ and temperatures from 25 °C to 350 °C. Results show that higher strain rates enhance yield strength through strain-rate hardening, while elevated temperatures lead to thermal softening and reduced mechanical stability. The Anand viscoplastic model accurately predicts these deformation behaviors. Microstructural analysis via scanning electron microscopy (SEM) and transmission electron microscopy (TEM) reveals localized deformation at 175 °C, with dislocations concentrated near the top surface and persistent porosity below, whereas at 350 °C, re-sintering and grain boundary diffusion create a denser microstructure. Phase-field fracture modeling further elucidates crack propagation, emphasizing the role of pore size and temperature. This combined experimental and modeling approach enhances understanding of viscoplastic deformation and fracture mechanisms in sintered Cu NPs, informing their use in interconnects, power electronics and thermal management systems.

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.

Industrial assembly lines are the heartbeat of modern manufacturing, where precision and efficiency are paramount. This paper introduces a novel hybrid Explainable artificial intelligence (XAI) approach to enhance monitoring and analysis in industrial assembly. By fusing the power of vision anomaly detection models with the clarity of the gradient tree boosting algorithm, this framework not only boosts defect detection accuracy but also provides transparent, actionable insights. This synergy transforms how operators and engineers interact with AI, fostering trust and enhancing operational excellence.

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 chapter, the past and present situations of the reliability domain are discussed. As of today, most industries are in the transfer from test-to-pass approaches to more advanced strategies. These strategies currently are to determine the reliability capability by applying (where possible) the test-to-failure concept, extending reliability qualification conformance tests beyond the required levels, and assessing any physical or electrical degradation of a product during those tests. New concepts are under investigation that focus on the physics of degradation. Progress in the area of reliability will never stop so as to reduce the amount (and cost) of product field failures.

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.

Silver sintering offers a promising landscape for Pb-free die attachment in electronics packaging. However, the sintered interface properties are highly process-dependent and deviate from bulk silver properties. Conventional measurement methods do not adequately capture the die-attach application geometry. Hence, this study introduces a novel methodology for characterizing thin bond-line interfaces with high-conductive materials. The transient heat flux impedance ΔZth(t, Δx) was measured between two thermally sensitive devices interconnected using pressureless Ag-sintering material. A correction factor was derived, based on thermal half-space principles, to account for non-uniform heat spreading over the die-attach interface. Experimental findings estimate an effective conductivity of ∼115W/mk for the pressureless Ag-sintered interface. The measurement results were validated by measuring a SAC305 soldered interface, which exhibited ∼55 W/mK, and a non-conductive epoxy interface of ∼2.5 W/mK. Voids on the die-attach layer, resulting from material processing, were identified to influence the interface thermal behavior. An uncertainty analysis was further discussed, emphasizing equipment tolerances, measurement sensitivity, and geometrical and thermal anisotropies. The article concludes with a comparative summary of the proposed methodology against conventional methods, highlighting differences in working principle, thickness range, measurement parameters, and their advantages and limitations.

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.

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).

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.

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..