Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing

Abstract

Task-oriented perspective on neuromorphic memristors for artificial intelligence computing. From material systems and device structures to applications, flexibility, and system integration. A critical roadmap toward scalable, manufacturable, and practical neuromorphic hardware. Task-oriented perspective on neuromorphic memristors for artificial intelligence computing. From material systems and device structures to applications, flexibility, and system integration. A critical roadmap toward scalable, manufacturable, and practical neuromorphic hardware. Artificial intelligence (AI) has advanced rapidly in recent years and has been widely applied in healthcare, intelligent sensing, machine perception, and image recognition. Neuromorphic computing, inspired by the structure and operating principles of the human brain, has emerged as a promising paradigm for building efficient, low-power, and adaptive information processing systems. In this review, we summarize the development of neuromorphic memristors from the perspectives of biological inspiration, representative material systems, device architectures, performance metrics, and artificial intelligence-oriented applications. Different from previous reviews that mainly focus on memristor materials, switching mechanisms, or neuromorphic functions separately, this article further emphasizes the relationships between memristor characteristics and distinct AI-oriented tasks, including AI acceleration, neuromorphic computing, intelligent sensing, and human–machine interaction. Finally, the major challenges and future opportunities of neuromorphic memristors are discussed from the viewpoints of device optimization, system integration, scalability, and practical application.