Gallium oxide is one of the most promising ultra-wide bandgap materials among the contemporary semiconductors. Fabrication of its conductive layers with superb crystal perfection is a great challenge for high power electronics. The present review analyzes the papers on homoepitaxial growth of thick monoclinic gallium oxide layers using MOCVD, HVPE and mist CVD. It has been shown that, by providing high growth rates and flexible doping control, each of them can ensure high structural quality combined with advanced electronic characteristics while being inexpensive.
As a prominent wide-bandgap semiconductor material, silicon carbide (SiC) is highly suitable for high-temperature, high-frequency, and high-power applications due to its excellent physical and electrical properties. In particular, for ultra-high-voltage power electronics, bipolar devices such as insulated-gate bipolar transistors and gate-turn-off thyristors, benefiting from conductivity modulation, demonstrate significantly superior performance compared to conventional silicon-based devices. However, the fabrication of these devices relies heavily on the availability of high-quality p-type SiC substrates. Currently, the performance of p-type SiC is limited by two major challenges: high resistivity and high defect density, which collectively hinder the development of ultra-high-voltage SiC power devices. Achieving p-type SiC single crystals with both low resistivity and high crystalline quality is therefore crucial to overcoming these challenges. This review focuses on three mainstream growth methods for p-type SiC single crystals: chemical vapor deposition (CVD), solution growth (SG), and physical vapor transport (PVT). It systematically summarizes recent advances in p-type doping technologies, addressing critical issues such as the high ionization energy of acceptor impurities, doping-induced lattice stress and defects formation, and low doping efficiency on the C-face. By analyzing the interplay between growth techniques and the resulting crystal characteristics, this work aims to shed light on the pathways toward achieving high-quality, low-resistivity p-type SiC single crystals and to stimulate further research in the crystal engineering of advanced semiconductor materials.
Magnetite (Fe3O4) nanoparticles have garnered significant attention due to their small size and high surface area, biocompatibility, and magnetic properties. This review offers an insightful and comprehensive discussion of recent advances in synthesis techniques and their impact on nanoparticle formation and characteristics. The diverse applications of Fe3O4 nanoparticles in medicine, modern photonics, energy storage, biosensing, catalysis, and environmental remediation are examined. Additionally, surface functionalization strategies designed to enhance stability, biocompatibility, and application-specific reactivity are outlined. Finally, current limitations are discussed with an outline for future research directions along with concluding perspectives on the continued development of Fe3O4 nanoparticle technologies.
Machine learning (ML) has become an increasingly powerful tool in crystal growth research, enabling new ways to model processes, optimize growth conditions, and automate characterization of crystalline materials. This review provides a comprehensive overview of ML applications in the growth of semiconductors and electronic materials, covering both bulk crystal growth techniques (Czochralski, Floating Zone, Directional Solidification, Top Seed Solution Growth, etc.) and epitaxial growth methods (MOCVD, MOVPE, etc.), along with related characterization methods (photoluminescence imaging, X-ray diffraction, microscopy, etc.). We trace the historical development of ML in crystal growth and highlight recent advances such as deep learning for defect detection, surrogate modeling for process optimization, and reinforcement learning for autonomous control. Key ML methodologies (e.g., decision trees, neural networks, Gaussian processes, and generative models) are discussed in the context of crystal growth tasks like property prediction, defect classification, clustering of microstructural features, process optimization, and more. We also detail how various data sources, from in situ sensor readings and furnace design parameters (e.g., geometry and materials), to process simulations and ex situ characterization data, can be integrated into ML frameworks for prediction, optimization, and control. Challenges specific to crystal growth (limited data, data heterogeneity, integration with physical models, and others) are examined, and we outline emerging trends and future outlook, including physics-informed ML and digital twin approaches for crystal growth. Overall, this work aims to demonstrate the significant progress achieved at the intersection of ML and crystal growth, while providing guidance for future research in this rapidly evolving interdisciplinary field.
Doped sapphire crystals (e.g., Ti: Al₂O₃, Cr: Al₂O₃, C: Al₂O₃, etc.), with their excellent physicochemical properties and tunable optoelectronic properties, are of great value for applications in the fields of laser devices, radiation detectors, and pyroelectric devices. In this paper, we systematically review the defect structures, preparation methods, and the modulation of the properties of different doped sapphire crystals, focus on the modulation mechanisms of different doping elements on the optical, mechanical, and laser properties of sapphire, and also details their optoelectronic applications in devices such as lasers, radiation detectors, and pyroelectric components. In addition, based on the current research progress, this paper also looks forward to the future development direction of doped sapphire crystals, including the optimization of the preparation technology for large-size and high-concentration uniformly doped crystals, as well as the potential for applications in emerging fields such as photonic chips and high-energy physics detectors, in anticipation of growing higher-quality doped sapphire crystals through the comprehensive improvement of the preparation technology and other aspects of the laser devices (e.g. photonic computing, LIDAR and other key hardware for artificial intelligence) and other applications.
