Jianhui Wang, Qingxin Chen, Jinhao Zhang, Jialin Meng, Tianyu Wang
{"title":"Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing","authors":"Jianhui Wang, Qingxin Chen, Jinhao Zhang, Jialin Meng, Tianyu Wang","doi":"10.1007/s40820-026-02322-5","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":714,"journal":{"name":"Nano-Micro Letters","volume":"19 1","pages":""},"PeriodicalIF":36.3000,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40820-026-02322-5.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nano-Micro Letters","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s40820-026-02322-5","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Nano-Micro Letters is a peer-reviewed, international, interdisciplinary, and open-access journal published under the SpringerOpen brand.
Nano-Micro Letters focuses on the science, experiments, engineering, technologies, and applications of nano- or microscale structures and systems in various fields such as physics, chemistry, biology, material science, and pharmacy.It also explores the expanding interfaces between these fields.
Nano-Micro Letters particularly emphasizes the bottom-up approach in the length scale from nano to micro. This approach is crucial for achieving industrial applications in nanotechnology, as it involves the assembly, modification, and control of nanostructures on a microscale.