用于储层计算的In2O3纳米纤维神经形态晶体管

IF 4.1 2区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Electron Device Letters Pub Date : 2023-07-03 DOI:10.1109/LED.2023.3290998

Chuanyu Fu;Hangyuan Cui;Shuo Ke;Yixin Zhu;Xiangjing Wang;Yang Yang;Changjin Wan;Qing Wan

{"title":"用于储层计算的In2O3纳米纤维神经形态晶体管","authors":"Chuanyu Fu;Hangyuan Cui;Shuo Ke;Yixin Zhu;Xiangjing Wang;Yang Yang;Changjin Wan;Qing Wan","doi":"10.1109/LED.2023.3290998","DOIUrl":null,"url":null,"abstract":"In this letter, we propose neuromorphic transistors employing indium oxide (In2O3) nanofibers as the channel layers. Basic synaptic function, such as short-term memory can be emulated by one nanofiber neuromorphic transistor. Nonlinear synaptic function and short-term memory characteristic of such neuromorphic transistors are favorable for reservoir computing (RC) system with high energy-efficiency. Ultra-low energy consumption (15 pJ per reservoir state) and ultra-high accuracy (100%) of speech digital recognition are realized based on such nanofiber neuromorphic transistors, proving a great potential of the RC system for intelligent processing tasks.","PeriodicalId":13198,"journal":{"name":"IEEE Electron Device Letters","volume":"44 8","pages":"1364-1367"},"PeriodicalIF":4.1000,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"In2O3 Nanofiber Neuromorphic Transistors for Reservoir Computing\",\"authors\":\"Chuanyu Fu;Hangyuan Cui;Shuo Ke;Yixin Zhu;Xiangjing Wang;Yang Yang;Changjin Wan;Qing Wan\",\"doi\":\"10.1109/LED.2023.3290998\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this letter, we propose neuromorphic transistors employing indium oxide (In2O3) nanofibers as the channel layers. Basic synaptic function, such as short-term memory can be emulated by one nanofiber neuromorphic transistor. Nonlinear synaptic function and short-term memory characteristic of such neuromorphic transistors are favorable for reservoir computing (RC) system with high energy-efficiency. Ultra-low energy consumption (15 pJ per reservoir state) and ultra-high accuracy (100%) of speech digital recognition are realized based on such nanofiber neuromorphic transistors, proving a great potential of the RC system for intelligent processing tasks.\",\"PeriodicalId\":13198,\"journal\":{\"name\":\"IEEE Electron Device Letters\",\"volume\":\"44 8\",\"pages\":\"1364-1367\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2023-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Electron Device Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10172080/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Electron Device Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10172080/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

摘要

在这封信中，我们提出了采用氧化铟(In2O3)纳米纤维作为沟道层的神经形态晶体管。基本的突触功能，例如短期记忆，可以用一个纳米纤维神经形态晶体管来模拟。这种神经形态晶体管的非线性突触功能和短时记忆特性有利于实现高能效的储层计算系统。基于该纳米纤维神经形态晶体管，实现了超低功耗(每个储层状态15 pJ)和超高精度(100%)的语音数字识别，证明了RC系统在智能处理任务中的巨大潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

In2O3 Nanofiber Neuromorphic Transistors for Reservoir Computing

In this letter, we propose neuromorphic transistors employing indium oxide (In2O3) nanofibers as the channel layers. Basic synaptic function, such as short-term memory can be emulated by one nanofiber neuromorphic transistor. Nonlinear synaptic function and short-term memory characteristic of such neuromorphic transistors are favorable for reservoir computing (RC) system with high energy-efficiency. Ultra-low energy consumption (15 pJ per reservoir state) and ultra-high accuracy (100%) of speech digital recognition are realized based on such nanofiber neuromorphic transistors, proving a great potential of the RC system for intelligent processing tasks.

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

IEEE Electron Device Letters 工程技术-工程：电子与电气

CiteScore

8.20

自引率

10.20%

发文量

551

审稿时长

1.4 months

期刊介绍： IEEE Electron Device Letters publishes original and significant contributions relating to the theory, modeling, design, performance and reliability of electron and ion integrated circuit devices and interconnects, involving insulators, metals, organic materials, micro-plasmas, semiconductors, quantum-effect structures, vacuum devices, and emerging materials with applications in bioelectronics, biomedical electronics, computation, communications, displays, microelectromechanics, imaging, micro-actuators, nanoelectronics, optoelectronics, photovoltaics, power ICs and micro-sensors.