Tungsten disulfide (WS2) has recently attracted considerable attention owing to its excellent physical, chemical, electronic, and optical properties, leading to increased research into its applications in electronic and optoelectronic devices. However, the oxidation of 2D material affects significantly its optical and electronic properties during storage or processing. This study employs density functional theory (DFT) to analyze the effects of natural and thermal oxidation on the optical and electronic properties of WS2 monolayer. First, the climbing-image nudged elastic band (cNEB) method is applied to analyze transitional states and the potential barriers of WS2 oxidation. It reveals that primarily involves the bonding of oxygen atoms with sulfur atoms, whereas thermal oxidation introduces both oxygen substitutions and generates oxygen vacancies. Second, the electron band structures after natural and thermal oxidation are comparably analyzed, which reveals that natural and thermal oxidation both can narrow the bandgap. Lastly, we investigate the optical properties of WS2 monolayer under different oxidation conditions. The results demonstrate that natural oxidation results in weakened light absorption and a blue shift relative to the pristine WS2, whereas thermal oxidation enhances absorption and induces a red shift. These findings underscore the importance of carefully managing oxidation conditions to effectively modulate the optoelectronic properties of WS2.@en

In the rapidly evolving era of information and intelligence,microelectronic devices are pivotal across various fields, such as mobile devices, big data computing, electric vehicles, and aerospace. However, the electrical performance of these devices often suffers due to residual stress from microelectronic manufacturing. This issue is compounded by the additional thermal stress that accumulates during device operation. Therefore, it is essential to understand, characterize, and control this residual stress to ensure the reliability and efficiency of microelectronic devices. Raman spectroscopy emerges as an invaluable tool for nondestructive, fast, noncontact, and precise testing of micro-scale mechanics, significantly aiding in stress and strain analysis within microelectronic manufacturing. This article aims to provide a thorough overview of the theory and application beyond a mere compilation of recent advances. Theoretically, it critically evaluates existing models that describe the Raman-stress relation. Practically, it explores the application of Raman spectroscopy in researching residual stress in various components, including substrate materials, epitaxial films, and packaging. Through a detailed examination of current applications, it highlights the significance of Raman spectroscopy in understanding micro-scale mechanics. Finally, it offers both theoretical and practical insights into the future developments of Raman-stress detection technology.@en

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.

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This work addresses a novel technique for selecting the best process parameters for the 4H–SiC epitaxial layer in a horizontal hot-wall chemical vapor reactor using a transient multi-physical (thermal-fluid-chemical) simulation model and combined with a machine-learning model. An experiment was performed to validate the feasibility of the numerical model. Secondly, a single-factor analysis was conducted to investigate the effects of process parameters, including the deposition temperature, inlet-flow volume, rotational speed of the susceptor, and cavity pressure, on the quality of the 4H–SiC epitaxial layer. Finally, a machine learning algorithm, the ant colony optimization-back propagation neural network (ACO–BPNN), was employed to develop the input/output model and optimize process parameters for obtaining a high-quality epitaxial layer and reducing the optimization cycle and costs. Notably, the optimized process was validated by real experiments, where the error between calculation and experiment is 4.03 % for deposition rate and 0.49 % for coefficient of variation, respectively. The results highlight the model as reliable and lay the foundation for the CVD growth of the 4H–SiC epitaxial layer.

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