Designing Memristive Materials for Artificial Dynamic Intelligence

Hardware architectures inspired by neural networks offer an effective approach to overcoming the limitations of conventional processors, particularly in high‐dimensional and energy‐efficient deep learning tasks. Among these, two‐terminal vertical memristors have gained attention as compact units capable of emulating synapses, neurons, and dendritic functions. Their intrinsic time‐dependent switching enables dynamic computation, which is essential for real‐time and large‐scale processing of unstructured data. However, the lack of theoretical guidance remains a barrier to scaling such bio‐inspired functions into integrated systems. This review provides a structured overview of approaches for analyzing and designing memristive systems applicable to neuromorphic hardware. It covers theoretical investigations based on first‐principles calculations, numerical simulations of device behavior, and physical modeling techniques for representative memristor devices. In addition, it discusses device‐level characteristics relevant to neural signal processing and their implications for system integration. Collectively, the review offers a comprehensive foundation for the development of materials and architectures that support efficient and scalable computing in next‐generation computings.