Emerging MXene-Based Memristors for In-Memory, Neuromorphic Computing, and Logic Operation,Advanced Functional Materials

当前位置： X-MOL 学术 › Adv. Funct. Mater. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Emerging MXene-Based Memristors for In-Memory, Neuromorphic Computing, and Logic Operation
Advanced Functional Materials ( IF 18.5 ) Pub Date : 2022-11-14 , DOI: 10.1002/adfm.202208320
Songtao Ling ₁ , Cheng Zhang ₁ , Chunlan Ma ₁ , Yang Li _{1,

2} , Qichun Zhang _{3,

4}

Affiliation

Confronted by the difficulties of the von Neumann bottleneck and memory wall, traditional computing systems are gradually inadequate for satisfying the demands of future data-intensive computing applications. Recently, memristors have emerged as promising candidates for advanced in-memory and neuromorphic computing, which pave one way for breaking through the dilemma of current computing architecture. Till now, varieties of functional materials have been developed for constructing high-performance memristors. Herein, the review focuses on the emerging 2D MXene materials-based memristors. First, the mainstream synthetic strategies and characterization methods of MXenes are introduced. Second, the different types of MXene-based memristive materials and their widely adopted switching mechanisms are overviewed. Third, the recent progress of MXene-based memristors for data storage, artificial synapses, neuromorphic computing, and logic circuits is comprehensively summarized. Finally, the challenges, development trends, and perspectives are discussed, aiming to provide guidelines for the preparation of novel MXene-based memristors and more engaging information technology applications.

中文翻译：

用于内存、神经形态计算和逻辑运算的新兴基于 MXene 的忆阻器

面对冯诺依曼瓶颈和内存墙的困境，传统的计算系统逐渐难以满足未来数据密集型计算应用的需求。最近，忆阻器已成为高级内存和神经形态计算的有前途的候选者，这为突破当前计算架构的困境铺平了道路。到目前为止，已经开发出多种功能材料来构建高性能忆阻器。在此，综述重点关注新兴的基于 2D MXene 材料的忆阻器。首先，介绍了MXenes的主流合成策略和表征方法。其次，概述了不同类型的基于 MXene 的忆阻材料及其广泛采用的开关机制。第三，全面总结了基于 MXene 的忆阻器在数据存储、人工突触、神经形态计算和逻辑电路方面的最新进展。最后，讨论了挑战、发展趋势和前景，旨在为制备新型基于 MXene 的忆阻器和更具吸引力的信息技术应用提供指导。

更新日期：2022-11-14

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南