CAREER: Scaling of Memristive Nanodevices and Arrays
职业:忆阻纳米器件和阵列的扩展
基本信息
- 批准号:1253073
- 负责人:
- 金额:$ 40万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-07-01 至 2019-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The research objectives of this CAREER proposal are to fabricate, test, characterize and understand memristive nanodevices and arrays with unprecedentedly small feature size and high packing density. The approaches are: 1) scaling of memristive nanodevices using nanoimprint lithograpy, electron beam lithography, directed self-assembly of block copolymer, and the combination of these next generation nanolithography technologies; 2) systematic electrical measurements of the devices/arrays of different length scale and elucidation of the scaling rules to sub-5 nm regime; and 3) physical characterization and underlying device physics studies at different length scales. Intellectual Merit As CMOS scaling approaches its limit, it is important that we develop new devices more versatile in functionality. Memristive devices are two-terminal passive electronic devices that use high and low resistance states instead of charge storage as '1's and '0's. As a result, the device scalability is not limited by the quantum effect but only dependent on how small the device one can make. Memristive devices have fast switch speed, overwrite ability without erase, low power consumption, high endurance and long data retention time. They are promising for applications in non-volatile memory, non-volatile logic, reconfigurable circuits and neuromorphic networks.This proposed research will lead to significantly smaller memristive nanodevices (3 nm) in the densest arrays (10 Tbits/in2), offering a universal solution to high-density non-volatile data storage and non-volatile logic. With devices of different dimensions available, full spectrum studies in device switching behavior (power consumption, endurance, switching speed, data retention time, etc.) as a function of size will be extended to sub-5 nm. Consequently, the proposed research will increase our understanding of the switching mechanisms/device physics and extend our knowledge to a physical regime not as yet achieved.Broader ImpactsThe proposed work will have significant scientific, educational and societal impact. The research will advance transformative device technologies for the integrated circuits (IC) industry, sustaining the U.S. competitiveness in high-technology areas. The education objectives of this CAREER proposal are to train next generation researchers and engineers, and to create motivating learning opportunities for students, STEM (Science, Technology, Engineering and Mathematics) teachers and the general public. The approaches to accomplishing these goals include: 1) innovative curricula design in semiconductors and nanotechnology for both graduate and undergraduate students; 2) inspiring research experience for undergraduate students, in particular women and minorities; and 3) engaging outreach activities such as Nanotechnology Summer Institute at UMass Amherst for K-12 teachers and promoting nanoscience using art among a much broader audience including the general public.
这项职业建议的研究目标是制造,测试,表征和理解富有特征大小和高包装密度的纳米式和阵列。方法是:1)使用纳米印刷岩石术,电子束光刻,块共聚物的定向自组装以及这些下一代纳米光刻技术的组合的纳米印刷纳米版本的缩放; 2)具有不同长度尺度的设备/阵列的系统电气测量以及将标度规则阐明为5 nm制度; 3)在不同长度尺度上的物理表征和基础设备物理研究。随着CMOS扩展即将达到极限,智力优点,重要的是,我们必须在功能上开发更通用的新设备。回忆设备是两端的被动电子设备,使用高电阻状态,而不是电荷存储为'1和'0。结果,设备的可伸缩性不受量子效应的限制,而仅取决于一个人可以制造的设备的小。回忆设备具有快速开关速度,覆盖能力,无需擦除,低功耗,高耐力和长时间的数据保留时间。它们对于在非挥发性记忆,非挥发性逻辑,可重新配置的电路和神经形态网络中的应用方面非常有希望。这项拟议的研究将导致较小的回忆性纳米电视(3 nm)在最密集的阵列中(10 Tbits/2),提供通用的解决方案,并提供通用的逻辑存储元素,并非具有高密度的非密度数据和非变色的数据。使用不同维度的设备,设备开关行为(功耗,耐力,开关速度,数据保留时间等)中的全光谱研究将扩展到低于5 nm。因此,拟议的研究将提高我们对开关机制/设备物理学的理解,并将我们的知识扩展到尚未达到的身体状态。BROADER影响拟议的工作将产生重大的科学,教育和社会影响。这项研究将推进综合电路(IC)行业的变革性设备技术,从而维持美国在高科技领域的竞争力。该职业建议的教育目标是培训下一代研究人员和工程师,并为学生,STEM(科学,技术,工程和数学)教师和公众创造激励的学习机会。实现这些目标的方法包括:1)针对研究生和本科生的半导体和纳米技术的创新课程设计; 2)为本科生,尤其是妇女和少数群体提供启发的研究经验; 3)参与宣传活动,例如UMass Amherst的纳米技术夏季研究所,为K-12老师提供纳米技术,并在包括普通大众在内的更广泛的受众中使用艺术来促进纳米科学。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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数据更新时间:2024-06-01
Qiangfei Xia其他文献
Alkylsiloxane self-assembled monolayer formation guided by nanoimprinted Si and SiO2 templates
纳米压印 Si 和 SiO2 模板引导烷基硅氧烷自组装单层形成
- DOI:10.1063/1.236092010.1063/1.2360920
- 发表时间:20062006
- 期刊:
- 影响因子:4
- 作者:A. A. Yasseri;Shashank Sharma;T. Kamins;Qiangfei Xia;S. Chou;R. PeaseA. A. Yasseri;Shashank Sharma;T. Kamins;Qiangfei Xia;S. Chou;R. Pease
- 通讯作者:R. PeaseR. Pease
Nanoimprint lithography enables memristor crossbars and hybrid circuits
纳米压印光刻技术实现忆阻器交叉开关和混合电路
- DOI:
- 发表时间:20152015
- 期刊:
- 影响因子:0
- 作者:Qiangfei Xia;Wei Wu;G. Jung;Shuang Pi;Peng Lin;Yong Chen;Xuema Li;Zhiyong Li;Shih;R. S. WilliamsQiangfei Xia;Wei Wu;G. Jung;Shuang Pi;Peng Lin;Yong Chen;Xuema Li;Zhiyong Li;Shih;R. S. Williams
- 通讯作者:R. S. WilliamsR. S. Williams
Learning with Resistive Switching Neural Networks
使用电阻开关神经网络学习
- DOI:
- 发表时间:20192019
- 期刊:
- 影响因子:0
- 作者:Mingyi Rao;Qiangfei Xia;J. Yang;Zhongrui Wang;Can Li;Hao Jiang;Rivu Midya;Peng Lin;Daniel Belkin;Wenhao Song;Shiva AsapuMingyi Rao;Qiangfei Xia;J. Yang;Zhongrui Wang;Can Li;Hao Jiang;Rivu Midya;Peng Lin;Daniel Belkin;Wenhao Song;Shiva Asapu
- 通讯作者:Shiva AsapuShiva Asapu
The secret order of disorder
混乱的秘密秩序
- DOI:10.1038/s41563-021-01110-310.1038/s41563-021-01110-3
- 发表时间:20212021
- 期刊:
- 影响因子:41.2
- 作者:Qiangfei Xia;J. Yang;Rivu MidyaQiangfei Xia;J. Yang;Rivu Midya
- 通讯作者:Rivu MidyaRivu Midya
In-Memory Computing with Memristor Arrays
使用忆阻器阵列进行内存计算
- DOI:10.1109/imw.2018.838883810.1109/imw.2018.8388838
- 发表时间:20182018
- 期刊:
- 影响因子:0
- 作者:Can Li;Daniel Belkin;Yunning Li;Peng Yan;Miao Hu;Ning Ge;Hao Jiang;Eric Montgomery;Peng Lin;Zhonguir Wang;J. Strachan;Mark D. Barnell;Qing Wu;R. S. Williams;J. Yang;Qiangfei XiaCan Li;Daniel Belkin;Yunning Li;Peng Yan;Miao Hu;Ning Ge;Hao Jiang;Eric Montgomery;Peng Lin;Zhonguir Wang;J. Strachan;Mark D. Barnell;Qing Wu;R. S. Williams;J. Yang;Qiangfei Xia
- 通讯作者:Qiangfei XiaQiangfei Xia
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Qiangfei Xia的其他基金
NSF-AoF: FET: Small: Ubiquitous in-sensor computing for adaptive intelligent systems
NSF-AoF:FET:小型:适用于自适应智能系统的无处不在的传感器内计算
- 批准号:21334752133475
- 财政年份:2021
- 资助金额:$ 40万$ 40万
- 项目类别:Standard GrantStandard Grant
Collaborative Research: ASCENT: 3D memristor convolutional kernels with diffusive memristor based reservoir for real-time machine learning
合作研究:ASCENT:3D 忆阻器卷积核,具有基于扩散忆阻器的存储库,用于实时机器学习
- 批准号:20237522023752
- 财政年份:2020
- 资助金额:$ 40万$ 40万
- 项目类别:Standard GrantStandard Grant
E2CDA: Type I: Collaborative Research: Energy-efficient analog computing with emerging memory devices
E2CDA:类型 I:协作研究:使用新兴存储设备的节能模拟计算
- 批准号:17402481740248
- 财政年份:2017
- 资助金额:$ 40万$ 40万
- 项目类别:Continuing GrantContinuing Grant
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