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.
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