Power semiconductor devices are essential to modern power and electronic systems as they govern the switching, conversion, and delivery of electric power. Silicon has long been the dominant material in this field. However, the rapid growth of electric vehicles, renewable energy systems, fast chargers, and compact power supplies has created stronger demand for higher voltage capability, lower loss, faster switching, and better thermal stability. This paper compares three important materials used in power devices: silicon (Si), silicon carbide (SiC), and gallium nitride (GaN). The discussion focuses on material properties that directly affect device behavior, including bandgap, breakdown field, thermal conductivity, and carrier transport. The analysis shows that silicon still offers strong advantages in cost, manufacturing maturity, and broad industrial use. SiC is more suitable for high-voltage and high-temperature operation, while GaN is especially attractive for high-frequency and fast-switching applications. The main conclusion is that SiC and GaN are expanding rapidly because they overcome performance limits inherent to silicon. However, material selection in power electronics still depends strongly on the specific requirements of each application.

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Silicon photonic device has compatibility property with CMOS technique; however, it is restricted by the non-direct bandgap of silicon, which brings about bad light emission efficiency and a disability to detect light with wavelengths that surpass 1.1 μm. Germanium makes the detection scope become wider toward the short-wave infrared area. Even so, its indirect bandgap still brings a barrier to the development of high-efficiency luminescence devices. Si-Ge-Sn alloys have become the foreground as a hopeful solution because the adding of tin can bring out a direct bandgap changing. The band gap of these alloy materials can be regulated in the interval from 0 to 0.8 eV, which covers the short-wave to mid-wave infrared spectral range. This review makes a summary of current research progresses about Si-Ge-Sn alloys which are used for infrared optoelectronic devices. It concentrates tightly on four key domains: theoretical energy band construction, material epitaxial growth, device utilizations, and important difficulties. Worthy achievements that attract attention include direct-bandgap Ge-Sn light-emitting lasers, middle-infrared light detecting devices, and the first-time on-chip integration work. The still existing questions, for example tin separation, heat stability, ohmic contact points, and restrictions of the CMOS heat budget, are discussed deeply, together with the future research directions. Defeating these difficulties will let out the whole capability of Si-Ge-Sn compound materials for low-cost, high-effectiveness infrared optoelectronic devices which are totally integrated on the silicon base platform.

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In recent years, with advances in astrophysical observation technology, direct imaging has become a powerful method for studying exoplanets, especially in the near-infrared band (11002526 nm). However, under the interference of strong stellar background light and instrumental residuals, planetary signals are extremely faint, typically requiring a contrast level of 10−5–10−8. Traditional image-processing methods such as Principal Component Analysis combined with Angular Differential Imaging (PCA-ADI) often fail to robustly de- tect planetary signals under low signal-to-noise ratio conditions. To address this, the PCA method is combined with three machine learning methods, including k-nearest neighbors (KNN), convolutional neural networks (CNN), and a discriminator based on generative ad- versarial networks (GAN). Using real observational data and precisely injected simulated-planet datasets from the VLT/SPHERE and Gemini/GPI exoplanet imaging challenge, a high-accuracy recognition model is constructed. Through an improved data-balancing strat- egy and enhanced preprocessing, remarkable performance is achieved in detection tasks: KNN reaches 97.83% accuracy and 98.81% F1 score, GAN achieves 100% precision and 96.43% recall, and CNN achieves 88.04% accuracy and 91.01% precision. This study successfully identifies exoplanet signals in eight datasets, verifies the effectiveness of machine-learning methods in high-contrast imaging tasks, and provides a new data-processing framework for future exoplanet detection.

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In recent years, deep reinforcement learning has seen notable advances in areas such as game playing and autonomous driving. How the underlying reasoning and decision-making processes actually develop and improve remains an open question that deserves closer attention. This paper starts from the most basic decision-making dilemma and traces the optimization path of deep reinforcement learning in reasoning and decision-making along the logical thread of sequential choice under complexity. Through a comparative look at classic strategies in the multi-armed bandit problem, the theoretical structure of Markov decision processes, and major deep reinforcement learning algorithms from the past five years, the paper shows that the heart of deep reinforcement learning optimization lies in a shift from model-based deduction toward interaction-based experience building. The results suggest that current algorithms continue to grapple with a trade-off between sample efficiency and generalization, while structural causal reasoning is drawing growing interest as a new research direction. The aim of this work is to offer a reference point for thinking about the nature of decision-making and for designing more effective learning algorithms.

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