{"title":"用于图像处理和模式识别的空位有序双过氧化物忆阻器","authors":"Wentong Li, Yanyun Ren, Tianwei Duan, Hao Tang, Hao Li, Kaihuan Zhang, Yu Sun, Xiaoyu Zhang, Weitao Zheng, Martyn A. McLachlan, Zhongrui Wang, Yuanyuan Zhou, Jiaqi Zhang","doi":"10.1016/j.matt.2024.10.006","DOIUrl":null,"url":null,"abstract":"High-performance memristors have emerged as efficient hardware for integrating noisy image recognition and noise reduction. Herein, we report a fast-switching memristor featuring tens of nanoseconds switching time fabricated using a vacancy-ordered double perovskite, Cs<sub>2</sub>TiBr<sub>6</sub> nanocrystals. The spatially ordered vacancies in the double perovskite facilitate the predictable formation and rupture of conductive filaments, which are explored through a comprehensive simulation using the finite element analysis physical model. These unique microscopic features suppress random conducting filament growth and enhance bromine vacancy diffusion, boosting memristor switching speed. A further study of synapse-like behaviors reveals that Cs<sub>2</sub>TiBr<sub>6</sub>-based memristors exhibit high robustness and reproducibility. We further developed the crossbar-array memristors as artificial neural networks for image denoising and classification, achieving a 10% increase in recognition accuracy for pre-denoised images over non-denoised samples. Our work highlights the potential of intrinsic vacancy-ordered memristive materials for advancing efficient, real-time, robust visual recognition.","PeriodicalId":388,"journal":{"name":"Matter","volume":null,"pages":null},"PeriodicalIF":17.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Vacancy-ordered double-perovskite-based memristors for image processing and pattern recognition\",\"authors\":\"Wentong Li, Yanyun Ren, Tianwei Duan, Hao Tang, Hao Li, Kaihuan Zhang, Yu Sun, Xiaoyu Zhang, Weitao Zheng, Martyn A. McLachlan, Zhongrui Wang, Yuanyuan Zhou, Jiaqi Zhang\",\"doi\":\"10.1016/j.matt.2024.10.006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"High-performance memristors have emerged as efficient hardware for integrating noisy image recognition and noise reduction. Herein, we report a fast-switching memristor featuring tens of nanoseconds switching time fabricated using a vacancy-ordered double perovskite, Cs<sub>2</sub>TiBr<sub>6</sub> nanocrystals. The spatially ordered vacancies in the double perovskite facilitate the predictable formation and rupture of conductive filaments, which are explored through a comprehensive simulation using the finite element analysis physical model. These unique microscopic features suppress random conducting filament growth and enhance bromine vacancy diffusion, boosting memristor switching speed. A further study of synapse-like behaviors reveals that Cs<sub>2</sub>TiBr<sub>6</sub>-based memristors exhibit high robustness and reproducibility. We further developed the crossbar-array memristors as artificial neural networks for image denoising and classification, achieving a 10% increase in recognition accuracy for pre-denoised images over non-denoised samples. Our work highlights the potential of intrinsic vacancy-ordered memristive materials for advancing efficient, real-time, robust visual recognition.\",\"PeriodicalId\":388,\"journal\":{\"name\":\"Matter\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":17.3000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Matter\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1016/j.matt.2024.10.006\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Matter","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1016/j.matt.2024.10.006","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Vacancy-ordered double-perovskite-based memristors for image processing and pattern recognition
High-performance memristors have emerged as efficient hardware for integrating noisy image recognition and noise reduction. Herein, we report a fast-switching memristor featuring tens of nanoseconds switching time fabricated using a vacancy-ordered double perovskite, Cs2TiBr6 nanocrystals. The spatially ordered vacancies in the double perovskite facilitate the predictable formation and rupture of conductive filaments, which are explored through a comprehensive simulation using the finite element analysis physical model. These unique microscopic features suppress random conducting filament growth and enhance bromine vacancy diffusion, boosting memristor switching speed. A further study of synapse-like behaviors reveals that Cs2TiBr6-based memristors exhibit high robustness and reproducibility. We further developed the crossbar-array memristors as artificial neural networks for image denoising and classification, achieving a 10% increase in recognition accuracy for pre-denoised images over non-denoised samples. Our work highlights the potential of intrinsic vacancy-ordered memristive materials for advancing efficient, real-time, robust visual recognition.
期刊介绍:
Matter, a monthly journal affiliated with Cell, spans the broad field of materials science from nano to macro levels,covering fundamentals to applications. Embracing groundbreaking technologies,it includes full-length research articles,reviews, perspectives,previews, opinions, personnel stories, and general editorial content.
Matter aims to be the primary resource for researchers in academia and industry, inspiring the next generation of materials scientists.