Ferroelectric Charged Domain-Wall Synapse for Neuromorphic Computing.

IF 9.6 1区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Nano Letters Pub Date : 2025-07-24 DOI:10.1021/acs.nanolett.5c02081

Chen Liang,Ye Wang,Yiming Liu,Xiaoming Shi,Ji Ma,Wael Ben Taazayet,Qinghua Liang,Huayu Yang,Yuanyuan Fan,Jiafang Li,Congli He,Ying Fu,Houbing Huang,Jing Wang,Ce-Wen Nan

{"title":"Ferroelectric Charged Domain-Wall Synapse for Neuromorphic Computing.","authors":"Chen Liang,Ye Wang,Yiming Liu,Xiaoming Shi,Ji Ma,Wael Ben Taazayet,Qinghua Liang,Huayu Yang,Yuanyuan Fan,Jiafang Li,Congli He,Ying Fu,Houbing Huang,Jing Wang,Ce-Wen Nan","doi":"10.1021/acs.nanolett.5c02081","DOIUrl":null,"url":null,"abstract":"Inspired by brain neural networks, integrated memory-computing devices are critical to meet the demands of big data and artificial intelligence. This work explores the quasi-continuous modulation of ferroelectric charged domain walls' conductance, which is confined in a topological quad-domain, allowing the charged domain walls to serve as neural synapses. The device mimics synaptic plasticity (long-term potentiation and depression) and shows paired impulse facilitation. In a designed ferroelectric domain-wall neural network, we demonstrate multiplicative, accumulation-additive operations between the input image and the stored response matrix, capable of image processing functions, including triclassification with 100% accuracy. In the neural network simulation, the MINST database and the Cifar-10 database achieve 98.7% and 95.1% recognition rates. The sub-nanosecond polarization switching and the ultrathin (3-5 nm) charged domain walls position them as a promising platform for advancing ultrafast and scalable synaptic devices for low-power (potentially reduced to 0.2 aJ with sub-nanosecond pulse durations) neuromorphic computing systems.","PeriodicalId":53,"journal":{"name":"Nano Letters","volume":"115 1","pages":""},"PeriodicalIF":9.6000,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nano Letters","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1021/acs.nanolett.5c02081","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Inspired by brain neural networks, integrated memory-computing devices are critical to meet the demands of big data and artificial intelligence. This work explores the quasi-continuous modulation of ferroelectric charged domain walls' conductance, which is confined in a topological quad-domain, allowing the charged domain walls to serve as neural synapses. The device mimics synaptic plasticity (long-term potentiation and depression) and shows paired impulse facilitation. In a designed ferroelectric domain-wall neural network, we demonstrate multiplicative, accumulation-additive operations between the input image and the stored response matrix, capable of image processing functions, including triclassification with 100% accuracy. In the neural network simulation, the MINST database and the Cifar-10 database achieve 98.7% and 95.1% recognition rates. The sub-nanosecond polarization switching and the ultrathin (3-5 nm) charged domain walls position them as a promising platform for advancing ultrafast and scalable synaptic devices for low-power (potentially reduced to 0.2 aJ with sub-nanosecond pulse durations) neuromorphic computing systems.

查看原文本刊更多论文

用于神经形态计算的铁电畴壁突触。

受大脑神经网络的启发，集成存储计算设备对于满足大数据和人工智能的需求至关重要。这项工作探索了铁电带电畴壁电导的准连续调制，其被限制在拓扑四畴中，允许带电畴壁作为神经突触。该装置模拟突触可塑性（长期增强和抑制），并显示成对的脉冲促进。在设计的铁电畴壁神经网络中，我们演示了输入图像和存储响应矩阵之间的乘法，累积-加性操作，能够实现图像处理功能，包括100%准确率的三分类。在神经网络仿真中，MINST数据库和Cifar-10数据库的识别率分别达到了98.7%和95.1%。亚纳秒极化开关和超薄（3-5纳米）带电畴壁使它们成为一个有前途的平台，用于推进超快和可扩展的突触器件，用于低功耗（亚纳秒脉冲持续时间可能降低到0.2 aJ）神经形态计算系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Nano Letters 工程技术-材料科学：综合

CiteScore

16.80

自引率

2.80%

发文量

1182

审稿时长

1.4 months

期刊介绍： Nano Letters serves as a dynamic platform for promptly disseminating original results in fundamental, applied, and emerging research across all facets of nanoscience and nanotechnology. A pivotal criterion for inclusion within Nano Letters is the convergence of at least two different areas or disciplines, ensuring a rich interdisciplinary scope. The journal is dedicated to fostering exploration in diverse areas, including: - Experimental and theoretical findings on physical, chemical, and biological phenomena at the nanoscale - Synthesis, characterization, and processing of organic, inorganic, polymer, and hybrid nanomaterials through physical, chemical, and biological methodologies - Modeling and simulation of synthetic, assembly, and interaction processes - Realization of integrated nanostructures and nano-engineered devices exhibiting advanced performance - Applications of nanoscale materials in living and environmental systems Nano Letters is committed to advancing and showcasing groundbreaking research that intersects various domains, fostering innovation and collaboration in the ever-evolving field of nanoscience and nanotechnology.