MAlSiN₃:Eu²⁺ (M = Ca, Sr) is commonly used in high-power phosphor-converted white-light-emitting diodes and laser diodes to promote their color-rendering index. However, the wide application of this phosphor is limited by the degradation of its luminescent properties in high-temperature, high-humidity, and high-sulfur-content environment. Here, the degradation mechanism of the (Sr,Ca)AlSiN₃:Eu²⁺ (SCASN) red phosphor under thermal-moisture-sulfur coupling conditions is investigated. Furthermore, by performing first-principles calculations, the hydrolysis mechanism on an atomic scale is assessed. The adsorption energy (E_ads) and charge transfer (ΔQ) results showed that H₂O chemically adsorbed on the (0 1 0), (3 1 0), and (0 0 1) surfaces of the CaAlSiN₃ (CASN) host lattice. The energy barrier for H₂O dissociation is only 29.73 kJ mol⁻¹ on the CASN (0 1 0) surface, indicating a high dissociation probability. The formation of NH₃, Ca(OH)₂, and CaAl₂Si₂O₈ is confirmed by H⁺ tended to combine with surface N atoms, while OH⁻ combined with the surface Al/Si or Ca atoms. Moreover, ab initio molecular dynamics simulations were performed to further understand the hydrolysis process. This work offers a guidance on the design and applications of luminescent materials in LED packages with higher reliability and stability requirements in harsh environment.

The power semiconductor joining technology through sintering of copper nanoparticles is well-suited for die attachment in wide bandgap (WBG) semiconductors, offering high electrical, thermal, and mechanical performances. However, sintered nanocopper will be prone to degradation resulting from corrosion in sulfur-containing corrosive environments such as offshore areas. In this study, experiments, including aging test and corrosion characterization, and simulations based on density functional theory (DFT) studies were conducted to explore the corrosion behavior and mechanism of elemental sulfur (S₈) and hydrogen sulfide (H₂S) on sintered nanocopper. The experimental results indicated that loose corrosion products were observed on the sintered nanocopper during the ageing process involving S₈, and compact layered corrosion products formed during the ageing process involving H₂S. Furthermore, similar corrosion product compositions (Cu₂O, Cu₂S, CuO, CuS, and potentially Cu₂SO₄ or CuSO₄) were observed in both the S₈- and H₂S-ageing processes. However, the S₈-ageing process exhibited more noticeable corrosion penetration. This was explained in simulations results: the unsaturated Cu sites on the oxide layer [Cu₂O(1 1 1)] of the sintered nanocopper could adsorb both H₂S and S₈, while the saturated Cu sites only exhibited the potential to adsorb S₈.

This paper presented integrated electromigration (EM) studies through experiment, theory, and simulation. First, extensive EM tests were performed using Blech and standard wafer-level electromigration acceleration test (SWEAT)-like structures, which were fabricated on four-inch wafers. Second, a molecular dynamics (MD) simulation-based diffusion-induced strain was incorporated into the existing coupled theory. Third, one-dimensional (1D) governing equations in terms of atomic concentration for un-passivated and passivated configurations were derived for void formation and growth, using a modified Eshelby's solution to consider the effect of passivation. Fourth, a systematic approach was established, including theoretical formulations and experimental methods, to obtain key material properties, i.e., critical atomic concentration and diffusivity. We then determined the material's properties from a specific set of experimental data, using aluminium (Al) as a carrier for demonstration. These properties were then used to predict the time to failure and void growth under various conditions. The theoretical results agreed well with the experimental data. Moreover, we theoretically determined the critical threshold products of current density and conductor length for the un-passivated and passivated configurations, respectively. Both experiment and theory showed that, in the absence of mechanical stress in un-passivated configurations, the atomic self-diffusion, which was opposite to electron wind, was significant in resisting EM development. However, when mechanical stress was present, such as in passivated configurations, stress migration played a dominant role in resisting EM development. Our numerical results showed that the current density exponent n in Black's law remained as 2 in the range of the current density greater than 0.2 MA/cm² and rapidly approached infinity at a low level of current density.

This paper presented a comprehensive experimental and simulation study for thermomigration (TM) accompanying electromigration (EM) at elevated current densities. Both Blech and standard wafer-level electromigration acceleration test (SWEAT)-like test structures, with aluminum (Al) as a carrier, were used for testing and analysis. In Part I of our study (Cui et al., 2023a), the experimental and numerical results with the current density of 1 MA/cm² were presented. We observed that Al stripes with a SWEAT structure did not show damage in the entire length, while Blech structures showed void and hillock formations only at the cathode and anode, respectively. The temperature gradient owing to Joule heating was neglected in our previous simulations, and the predicted results agreed well with the experimental observations. However, we have not theoretically verified the effect of the temperature gradient. In this paper, we first reported the new experimental data under the elevated current densities of 3 and 5 MA/cm². In both Blech and SWEAT structures, the spreading of voids in the middle region of conductors was observed. Moreover, in Blech structures, voiding in the middle region occurred after a period of time when voids/hillocks were formed at the cathode and anode, while the SWEAT structures did not show damage at the two ends. Next, based on the coupled 3D theory (Cui et al., 2023a), new analytical one-dimensional (1D) solutions were derived for the Blech and SWEAT structures in the un-passivated configuration considering TM. We found that TM played a significant role in the EM development in the middle of conductors under the elevated current density. The numerical results were in excellent agreement with the experimental data with the consideration of TM. We further established new EM failure's threshold criteria for the SWEAT structures in the form of the product of current density and square of conductor length. This is a major departure from the original Blech's theory in which only mechanical stress gradient was considered. We also studied the acceleration factor of the current density exponent and presented an insight into failure mechanisms associated with TM.

As a promising technology for high-power and high-temperature power electronics packaging, nanocopper (nanoCu) paste sintering has recently received increasing attention as a die-attachment. The high-temperature deformation of sintered nanoCu paste and its underlying mechanisms challenge the reliability of high-power electronics packaging. In this study, the tensile deformation behaviors of sintered nanoCu paste were firstly characterized by high-temperature tensile tests performed at various temperatures and strain rates ranging from 180 °C to 360 °C, 1 × 10⁻⁴ s⁻¹ to 1 × 10⁻³ s⁻¹ respectively. It was found that the elastic modulus and tensile strength decreased at the higher tensile temperature while the ductility increased accordingly. The highest elastic modulus and tensile strength results were 12.15 GPa and 46.97 MPa, respectively. Second, failure analysis was conducted based on the fracture surface after tensile testing. Recrystallization was revealed as the main factor for ductility improvement. Subsequently, an Anand model was fitted by stress-strain curves to describe the tensile constitutive behavior of the sintered nanoCu paste. Multi-scale modelling techniques also investigated the impact of tensile temperature and strain rate on the tensile response. Molecular dynamics simulation was implemented using a hemispherical Cu nanoparticle model to reveal the properties from an atomistic perspective. In addition, a two-dimensional equivalent model was further established by using a stochastically distributed void morphology. The multi-scale modelling techniques successfully describe the evolution of tensile response to the different tensile temperatures and strain rates. Besides, the equivalent model with random void morphology was demonstrated as the finite element simulation results were highly consistent with the high-temperature tensile experiments.

In this paper, we apply the Eshelby's solution to study the effect of passivation layer on electromigration (EM) failure in a conductor. The passivation layer is considered as an elastic material, not a rigid layer anymore. Thus, the deformation and stress evolution in the conductor during EM are related to the mechanical property of the passivation layer. One-dimensional (1D) analytical solution for the passivated conductor is obtained. The numerical results show that the conductor covered with the stiffer passivation layer has much less EM damage. And the steady-state solution shows that the magnitude of (jL)c increases with increasing Young's modulus of passivation material. The present study provides a way to predict the EM performances taking into account various passivation materials.

Considerable advancements in power semiconductor devices have resulted in such devices being increasingly adopted in applications of energy generation, conversion, and transmission. Hence, we proposed a fan-out panel-level packaging (FOPLP) design for 30-V Si-based metal-oxide-semiconductor field-effect transistor (MOSFET). To achieve superior reliability of packaging, we applied the nondominated sorting genetic algorithm with elitist strategy (NSGA-II) and ant colony optimization-backpropagation neural network (ACO-BPNN) to optimize the design of redistribution layer (RDL) in FOPLP. We first quantified the thermal resistance and thermomechanical coupling stress of the designed package under thermal cycling loading. Next, NSGA-II and ACO-BPNN were used to optimize the size of the RDL blind via. Finally, the effectiveness of the proposed reliability optimization methods was verified by performing thermal shock reliability aging tests on the prepared devices.

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

Understanding the atomic diffusion features in metallic material is significant to explain the diffusion-controlled physical processes. In this paper, using electromigration experiments and molecular dynamic (MD) simulations, we investigate the effects of grain size and temperature on the self-diffusion of polycrystalline aluminium (Al). The mass transport due to electromigration are accelerated by increasing temperature and decreasing grain size. Magnitudes of effective diffusivity (Deff) and grain boundary diffusivity (DGBs) are experimentally determined, in which theDeffchanges as a function of grain size and temperature, butDGBsis independent of the grain size, only affected by the temperature. Moreover, MD simulations of atomic diffusion in polycrystalline Al demonstrate those observations from experiments. Based on MD results, the Arrhenius equation ofDGBsand empirical formula of the thickness of grain boundaries at various temperatures are obtained. In total,DeffandDGBsobtained in the present study agree with literature results, and a comprehensive result of diffusivities related to the grain size is presented.

In this paper, we presented several practical aspects for building robust and reliable finite element models in thermomechanical modeling in electronics packaging using finite element analysis. Firstly, for layered or patterned structures, a homogenized equivalent model, with equivalent orthotropic material properties, gives excellent agreement with the exact finite element model solutions. Such a simplified finite element model provides an efficient way for structural parameter optimization. Secondly, the finite element mesh should keep the fixed size and shape at the location of interest where the singular point exists. This approach provides a simple way for relative stress comparison in different designs, although the absolute value of stress components has no actual meaning. Thirdly, to further eliminate the mesh dependency, the volume averaging method can be used. We extended the local volume averaging method for large-area die attach problems. Fourthly, in this paper, we presented a comparison study between linear elastic and nonlinear viscoplastic analysis, and demonstrated that in some cases, two different types of analysis give opposite trend results. Lastly, we demonstrated that with the use of different stress components, the conclusions may be different. We also provided an ANSYS APDL script in the supplemental material as a benchmark example.

In this paper, stability and mechanistic simulations for a four-beam-mass-based MEMS gravimeter were conducted, and guidelines for the gravimeter design were proposed. Based on a prototyped MEMS device, the nonlinear finite element model was validated first against the experimental results. Then, we demonstrated three different scenarios in design that have three distinct modes of deformation: the mode with buckling (case 1), the mode without buckling but with a single zero-stiffness point (case 2), and the mode without both buckling and zero-stiffness point (case 3). Both case 1 and case 2 presented an unstable and sensitive region, in which a tiny perturbation could result in a rapid increase of the resonance frequency. Case 3, on the other hand, could provide a stable and low resonance frequency with a linear relationship between the displacement and gravitational acceleration. An optimized design of a beam/spring-mass-based relative gravimeter could be achieved using the above guidelines.

Owing to its low cost and high efficiency, the blue light-emitting diode (LED) chip covered with the Y₃Al₅O₁₂:Ce³⁺ (YAG) phosphor has become a mainstream technology of high power white LED packaging. However, phosphors are often susceptible to degradation under high-temperature and high-humidity conditions, which directly affects the optical and color stability of the white LED package. In this study, the moisture resistance of YAG phosphor was evaluated through both a short-term water-immersion test and long-term aging tests, i.e. 85 °C&85% Relative Humidity (RH) test and high-temperature and saturated humidity test. The phosphor hydrolysis mechanism was then revealed by characterizing the phosphor emission spectrum, photothermal property, crystal structure, micromorphology, and chemical element composition.

A new panel-level silicon carbide (SiC) metal oxide semiconductor field effect transistor (MOSFET) power module was developed by using the fan-out and embedded chip technologies. To achieve the more effective thermal management and higher reliability under thermal cycling, a new optimization method called Ant colony optimization-back propagation neural network (ACO-BPNN) was developed for optimizing SiC modules, and contrast it with the Response Surface Method (RSM). First, the heat dissipations of SiC MOSFET with different redistribution layer (RDL) materials were simulated through the ANSYS finite element simulation. Then, the RSM was adopted to design the experiments for optimization. Third, the optimized design considering both junction temperature and thermal-mechanical stress is obtained using RSM and ACO-BPNN. The results show that: 1) compared with nano-silver, copper has a relatively good heat dissipation effect, but nano-silver has a better thermodynamic performance, and 2) ACO-BPNN can provide more accurate optimization results without having to construct a fitness function like RSM. After optimization, the thermal management and the thermal-mechanical stress can be improved by about 3% and 11%, respectively.

In this paper, tin oxidation (SnO x )/tin-sulfide (SnS) heterostructures are synthesized by the post-oxidation of liquid-phase exfoliated SnS nanosheets in air. We comparatively analyzed the NO2 gas response of samples with different oxidation levels to study the gas sensing mechanisms. The results show that the samples oxidized at 325 °C are the most sensitive to NO2 gas molecules, followed by the samples oxidated at 350 °C, 400 °C and 450 °C. The repeatabilities of 350 °C samples are better than that of 325 °C, and there is almost no shift in the baseline. Thus this work systematically analyzed the gas sensing performance of SnO x/SnS-based sensor oxidized at 350 °C. It exhibits a high response of 171% towards 1 ppb NO2, a wide detecting range (from 1 ppb to 1 ppm), and an ultra-low theoretical detection limit of 5 ppt, and excellent repeatability at room temperature. The sensor also shows superior gas selectivity to NO2 in comparison to several other gas molecules, such as NO, H2, SO2, CO, NH3, and H2O. After X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscope, and electron paramagnetic resonance characterizations combining first principle analysis, it is found that the outstanding NO2 sensing behavior may be attributed to three factors: The Schottky contact between electrodes and SnO x/SnS; active charge transfer in the surface and the interface layer of SnO x/SnS heterostructures; and numerous oxygen vacancies generated during the post-oxidation process, which provides more adsorption sites and superior bandgap modulation. Such a heterostructure-based room-temperature sensor can be fabricated in miniaturized size with low cost, making it possible for large-scale applications.

The full-spectrum white light-emitting diode (LED) emits light with a broad wavelength range by mixing all lights from multiple LED chips and phosphors. Thus, it has great potentials to be used in healthy lighting, high resolution displays, plant lighting with higher color rendering index close to sunlight and higher color fidelity index. The spectral power distribution (SPD) of light source, representing its light quality, is always dynamically controlled by complex electrical and thermal loadings when the light source operates under usage conditions. Therefore, a dynamic prediction of SPD for the full-spectrum white LED has become a hot but challenging research topic in the high quality lighting design and application. This paper proposes a dynamic SPD prediction method for the full-spectrum white LED by integrating the SPD decomposition approach with the artificial neural network (ANN) based machine learning method. Firstly, the continuous SPDs of a full-spectrum white LED driven by an electrical-thermal loading matrix are discretized by the multi-peak fitting with Gaussian model as the relevant spectral characteristic parameters. Then, the Back Propagation (BP) and Genetic Algorithm-Back Propagation (GA-BP) NNs are proposed to predict the spectral characteristic parameters of LEDs operated under any usage conditions. Finally, the dynamically predicted spectral characteristic parameters are used to reconstruct the SPDs. The results show that: (1) The spectral characteristic parameters obtained by fitting with the Gaussian model can be used to represent the emission lights from multiple chips and phosphors in a full-spectrum white LED; (2) The prediction errors of both BP NN and GA-BP NN can be controlled at low level, that is to say, our proposed method can achieve a highly accurate SPD dynamic prediction for the full-spectrum white LED when it operates under different operation mission profiles.

Light-emitting diodes (LEDs) are among the key innovations that have revolutionized the lighting industry, due to their versatility in applications, higher reliability, longer lifetime, and higher efficiency compared with other light sources. The demand for increased lifetime and higher reliability has attracted a significant number of research studies on the prognostics and lifetime estimation of LEDs, ranging from the traditional failure data analysis to the latest degradation modeling and machine learning based approaches over the past couple of years. However, there is a lack of reviews that systematically address the currently evolving machine learning algorithms and methods for fault detection, diagnostics, and lifetime prediction of LEDs. To address those deficiencies, a review on the diagnostic and prognostic methods and algorithms based on machine learning that helps to improve system performance, reliability, and lifetime assessment of LEDs is provided. The fundamental principles, pros and cons of methods including artificial neural networks, principal component analysis, hidden Markov models, support vector machines, and Bayesian networks are presented. Finally, discussion on the prospects of the machine learning implementation from LED packages, components to system level reliability analysis, potential challenges and opportunities, and the future digital twin technology for LEDs lifetime analysis is provided.