Zheng Wang, Kangli Xu, Jialin Meng, Bo Feng, Tianyu Wang
{"title":"Carbon-based memristors for neuromorphic computing","authors":"Zheng Wang, Kangli Xu, Jialin Meng, Bo Feng, Tianyu Wang","doi":"10.1063/5.0260582","DOIUrl":null,"url":null,"abstract":"Driven by the rapid advancement of the Internet of Things and artificial intelligence, computational power demands have experienced an exponential surge, thereby accentuating the inherent limitations of the conventional von Neumann architecture. Neuromorphic computing memristors are emerging as a promising solution to overcome this bottleneck. Among various material-based memristors, carbon-based memristors (CBMs) are particularly attractive due to their biocompatibility, flexibility, and stability, which make them well suited for next-generation neuromorphic applications. This review summarizes the recent advancements in CBMs and proposes potential application scenarios in neuromorphic computing. Representative CBMs and preparation methods of carbon-based materials in different dimensions (0D, 1D, 2D, and 3D) are presented, followed by structural, storage, and synaptic plasticity testing and switching mechanisms. The neural network architecture built by CBMs is summarized for image processing, wearable electronics, and three-dimensional integration. Finally, the future challenges and application prospects of CBMs are reviewed and summarized.","PeriodicalId":8200,"journal":{"name":"Applied physics reviews","volume":"37 1","pages":""},"PeriodicalIF":11.6000,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied physics reviews","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1063/5.0260582","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
Driven by the rapid advancement of the Internet of Things and artificial intelligence, computational power demands have experienced an exponential surge, thereby accentuating the inherent limitations of the conventional von Neumann architecture. Neuromorphic computing memristors are emerging as a promising solution to overcome this bottleneck. Among various material-based memristors, carbon-based memristors (CBMs) are particularly attractive due to their biocompatibility, flexibility, and stability, which make them well suited for next-generation neuromorphic applications. This review summarizes the recent advancements in CBMs and proposes potential application scenarios in neuromorphic computing. Representative CBMs and preparation methods of carbon-based materials in different dimensions (0D, 1D, 2D, and 3D) are presented, followed by structural, storage, and synaptic plasticity testing and switching mechanisms. The neural network architecture built by CBMs is summarized for image processing, wearable electronics, and three-dimensional integration. Finally, the future challenges and application prospects of CBMs are reviewed and summarized.
期刊介绍:
Applied Physics Reviews (APR) is a journal featuring articles on critical topics in experimental or theoretical research in applied physics and applications of physics to other scientific and engineering branches. The publication includes two main types of articles:
Original Research: These articles report on high-quality, novel research studies that are of significant interest to the applied physics community.
Reviews: Review articles in APR can either be authoritative and comprehensive assessments of established areas of applied physics or short, timely reviews of recent advances in established fields or emerging areas of applied physics.