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 , 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","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 , 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\",\"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}
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.