面向双模式智能视觉的平面内铁电重构界面

Yichen Cai , Yizhou Jiang , Xiaofei Yue , Chenxu Sheng , Yajie Qin , Shisheng Xiong , Yiqiang Zhan , Zhi-Jun Qiu , Ran Liu , Wei Chen , Zheng Liu , Laigui Hu , Chunxiao Cong
{"title":"面向双模式智能视觉的平面内铁电重构界面","authors":"Yichen Cai ,&nbsp;Yizhou Jiang ,&nbsp;Xiaofei Yue ,&nbsp;Chenxu Sheng ,&nbsp;Yajie Qin ,&nbsp;Shisheng Xiong ,&nbsp;Yiqiang Zhan ,&nbsp;Zhi-Jun Qiu ,&nbsp;Ran Liu ,&nbsp;Wei Chen ,&nbsp;Zheng Liu ,&nbsp;Laigui Hu ,&nbsp;Chunxiao Cong","doi":"10.1016/j.nxnano.2024.100052","DOIUrl":null,"url":null,"abstract":"<div><p>With the rapid progress of intelligent vision of the Internet of Things, the gap between the resultant high-level demands and conventional silicon-based hardware architectures is continuously widening. Biomimetic artificial neural networks (ANNs) have recently been proposed to solve this problem. However, they still face the predicaments in uniformity, heterogeneous integration, and hardware implementation, etc. Here we demonstrate an intelligent vision ANN with two-dimensional material (2DM)/molecular ferroelectric (MF) bilayer electronic/optoelectronic memristors. In contrast to conventional ferroelectric transistors, in-plane ferroelectric polarization was also found to enable a simpler two-terminal structure with a lateral p/n-type doping in the adjacent 2DM layer. After a demonstration of fabricated array and board-level driving circuits, an image recognition and classification task is proposed, reaching an accuracy of 85.2%, which implies great potential towards multi-modal artificial intelligent vision systems.</p></div>","PeriodicalId":100959,"journal":{"name":"Next Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949829524000135/pdfft?md5=310c388c456c0aacd378d9f9a89030ee&pid=1-s2.0-S2949829524000135-main.pdf","citationCount":"0","resultStr":"{\"title\":\"In-plane ferroelectric-reconfigured interface towards dual-modal intelligent vision\",\"authors\":\"Yichen Cai ,&nbsp;Yizhou Jiang ,&nbsp;Xiaofei Yue ,&nbsp;Chenxu Sheng ,&nbsp;Yajie Qin ,&nbsp;Shisheng Xiong ,&nbsp;Yiqiang Zhan ,&nbsp;Zhi-Jun Qiu ,&nbsp;Ran Liu ,&nbsp;Wei Chen ,&nbsp;Zheng Liu ,&nbsp;Laigui Hu ,&nbsp;Chunxiao Cong\",\"doi\":\"10.1016/j.nxnano.2024.100052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the rapid progress of intelligent vision of the Internet of Things, the gap between the resultant high-level demands and conventional silicon-based hardware architectures is continuously widening. Biomimetic artificial neural networks (ANNs) have recently been proposed to solve this problem. However, they still face the predicaments in uniformity, heterogeneous integration, and hardware implementation, etc. Here we demonstrate an intelligent vision ANN with two-dimensional material (2DM)/molecular ferroelectric (MF) bilayer electronic/optoelectronic memristors. In contrast to conventional ferroelectric transistors, in-plane ferroelectric polarization was also found to enable a simpler two-terminal structure with a lateral p/n-type doping in the adjacent 2DM layer. After a demonstration of fabricated array and board-level driving circuits, an image recognition and classification task is proposed, reaching an accuracy of 85.2%, which implies great potential towards multi-modal artificial intelligent vision systems.</p></div>\",\"PeriodicalId\":100959,\"journal\":{\"name\":\"Next Nanotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2949829524000135/pdfft?md5=310c388c456c0aacd378d9f9a89030ee&pid=1-s2.0-S2949829524000135-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Next Nanotechnology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949829524000135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Next Nanotechnology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949829524000135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

随着物联网智能愿景的快速发展,由此产生的高层次需求与传统硅基硬件架构之间的差距正在不断扩大。最近,仿生人工神经网络(ANN)被提出来解决这一问题。然而,它们仍然面临着统一性、异构集成和硬件实现等方面的困境。在此,我们展示了一种采用二维材料(2DM)/分子铁电(MF)双层电子/光电忆阻器的智能视觉神经网络。与传统的铁电晶体管相比,我们发现平面内的铁电极化也能使相邻 2DM 层中的横向 p/n 型掺杂的双端结构变得更简单。在演示了制造的阵列和板级驱动电路后,提出了一项图像识别和分类任务,准确率达到 85.2%,这意味着多模态人工智能视觉系统具有巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In-plane ferroelectric-reconfigured interface towards dual-modal intelligent vision

With the rapid progress of intelligent vision of the Internet of Things, the gap between the resultant high-level demands and conventional silicon-based hardware architectures is continuously widening. Biomimetic artificial neural networks (ANNs) have recently been proposed to solve this problem. However, they still face the predicaments in uniformity, heterogeneous integration, and hardware implementation, etc. Here we demonstrate an intelligent vision ANN with two-dimensional material (2DM)/molecular ferroelectric (MF) bilayer electronic/optoelectronic memristors. In contrast to conventional ferroelectric transistors, in-plane ferroelectric polarization was also found to enable a simpler two-terminal structure with a lateral p/n-type doping in the adjacent 2DM layer. After a demonstration of fabricated array and board-level driving circuits, an image recognition and classification task is proposed, reaching an accuracy of 85.2%, which implies great potential towards multi-modal artificial intelligent vision systems.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信