基于双钙钛矿Bi2FeCoO6的铁电记忆电阻器用于突触性能和人类表达识别存储

Dong-Ping Yang , Wen-Min Zhong , Jun Li , Xin-Gui Tang , Qi-Jun Sun , Qiu-Xiang Liu , Yan-Ping Jiang
{"title":"基于双钙钛矿Bi2FeCoO6的铁电记忆电阻器用于突触性能和人类表达识别存储","authors":"Dong-Ping Yang ,&nbsp;Wen-Min Zhong ,&nbsp;Jun Li ,&nbsp;Xin-Gui Tang ,&nbsp;Qi-Jun Sun ,&nbsp;Qiu-Xiang Liu ,&nbsp;Yan-Ping Jiang","doi":"10.1016/j.mtelec.2024.100133","DOIUrl":null,"url":null,"abstract":"<div><div>This study reports for the first time the application of double perovskite thin-film devices based on the Bi<sub>2</sub>FeCoO<sub>6</sub> (BFCO) compound in non-volatile ferroelectric memristors. By spin-coating BFCO onto an N-type silicon (N-Si) substrate, a P-N junction was formed, yielding a thin-film device with ferroelectric properties. The device demonstrated a maximum polarization value of 46.09 μC/cm² and a high switching ratio of 293, along with excellent long-term stability (over 7 days) and high repeatability (1000 cycles). Furthermore, we investigated the synaptic characteristics of the device, including short-term plasticity, paired-pulse facilitation, and long-term potentiation/inhibition behaviors. By designing a confusion matrix recognition scenario with a binary neural network, we validated the potential of double perovskite ferroelectric memristors in intelligent learning applications. Additionally, leveraging the synaptic plasticity of the device, we developed a modal storage memory and recognition system for human emotions. This work not only provides new insights into the development of high-performance double perovskite ferroelectric memristors but also lays the foundation for optimizing synaptic performance in intelligent learning applications.</div></div>","PeriodicalId":100893,"journal":{"name":"Materials Today Electronics","volume":"11 ","pages":"Article 100133"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ferroelectric memristors based on double perovskite Bi2FeCoO6 for synaptic performance and human expression recognition storage\",\"authors\":\"Dong-Ping Yang ,&nbsp;Wen-Min Zhong ,&nbsp;Jun Li ,&nbsp;Xin-Gui Tang ,&nbsp;Qi-Jun Sun ,&nbsp;Qiu-Xiang Liu ,&nbsp;Yan-Ping Jiang\",\"doi\":\"10.1016/j.mtelec.2024.100133\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study reports for the first time the application of double perovskite thin-film devices based on the Bi<sub>2</sub>FeCoO<sub>6</sub> (BFCO) compound in non-volatile ferroelectric memristors. By spin-coating BFCO onto an N-type silicon (N-Si) substrate, a P-N junction was formed, yielding a thin-film device with ferroelectric properties. The device demonstrated a maximum polarization value of 46.09 μC/cm² and a high switching ratio of 293, along with excellent long-term stability (over 7 days) and high repeatability (1000 cycles). Furthermore, we investigated the synaptic characteristics of the device, including short-term plasticity, paired-pulse facilitation, and long-term potentiation/inhibition behaviors. By designing a confusion matrix recognition scenario with a binary neural network, we validated the potential of double perovskite ferroelectric memristors in intelligent learning applications. Additionally, leveraging the synaptic plasticity of the device, we developed a modal storage memory and recognition system for human emotions. This work not only provides new insights into the development of high-performance double perovskite ferroelectric memristors but also lays the foundation for optimizing synaptic performance in intelligent learning applications.</div></div>\",\"PeriodicalId\":100893,\"journal\":{\"name\":\"Materials Today Electronics\",\"volume\":\"11 \",\"pages\":\"Article 100133\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Today Electronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772949424000457\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Today Electronics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772949424000457","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

本研究首次报道了基于Bi2FeCoO6 (BFCO)化合物的双钙钛矿薄膜器件在非易失性铁电记忆电阻器中的应用。通过将BFCO自旋涂覆在n型硅(N-Si)衬底上,形成了P-N结,产生了具有铁电性能的薄膜器件。该器件的最大极化值为46.09 μC/cm²,开关比为293,具有良好的长期稳定性(超过7天)和高重复性(1000次循环)。此外,我们还研究了该装置的突触特性,包括短期可塑性、配对脉冲促进和长期增强/抑制行为。通过设计一个二元神经网络的混淆矩阵识别场景,我们验证了双钙钛矿铁电记忆电阻器在智能学习应用中的潜力。此外,利用该装置的突触可塑性,我们开发了一种人类情感的模态存储记忆和识别系统。这项工作不仅为高性能双钙钛矿铁电记忆电阻器的开发提供了新的见解,而且为优化智能学习应用中的突触性能奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Ferroelectric memristors based on double perovskite Bi2FeCoO6 for synaptic performance and human expression recognition storage

Ferroelectric memristors based on double perovskite Bi2FeCoO6 for synaptic performance and human expression recognition storage
This study reports for the first time the application of double perovskite thin-film devices based on the Bi2FeCoO6 (BFCO) compound in non-volatile ferroelectric memristors. By spin-coating BFCO onto an N-type silicon (N-Si) substrate, a P-N junction was formed, yielding a thin-film device with ferroelectric properties. The device demonstrated a maximum polarization value of 46.09 μC/cm² and a high switching ratio of 293, along with excellent long-term stability (over 7 days) and high repeatability (1000 cycles). Furthermore, we investigated the synaptic characteristics of the device, including short-term plasticity, paired-pulse facilitation, and long-term potentiation/inhibition behaviors. By designing a confusion matrix recognition scenario with a binary neural network, we validated the potential of double perovskite ferroelectric memristors in intelligent learning applications. Additionally, leveraging the synaptic plasticity of the device, we developed a modal storage memory and recognition system for human emotions. This work not only provides new insights into the development of high-performance double perovskite ferroelectric memristors but also lays the foundation for optimizing synaptic performance in intelligent learning applications.
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
CiteScore
2.10
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