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 , Wen-Min Zhong , Jun Li , Xin-Gui Tang , Qi-Jun Sun , Qiu-Xiang Liu , 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 , Wen-Min Zhong , Jun Li , Xin-Gui Tang , Qi-Jun Sun , Qiu-Xiang Liu , 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}
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.