Abstract: This paper systematically examines the application and implementation of hafnium-based materials in artificial neural networks, offering both theoretical insights and experimental foundations for the hardware realization of non-von Neumann architectures. Material properties, device performance of HfO₂-based FTJs, and their potential applications in in-memory computing are thoroughly analyzed. First, the research background, global advancements, and emerging trends in hafnium-based materials are reviewed. Subsequently, the operational principles of FTJs are explored, with an emphasis on critical metrics such as switching ratio, endurance, and multi-state storage, alongside current strategies to enhance their ferroelectric characteristics. Finally, the integration of hafnium-based FTJs into neural networks is evaluated, and potential future development pathways are projected.