A Framework of Algorithms & Simulator for Quantum Mechanical Modeling of Nanodevices

算法框架

基本信息

  • 批准号:
    1231927
  • 负责人:
  • 金额:
    $ 34万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-09-01 至 2017-08-31
  • 项目状态:
    已结题

项目摘要

Objective: The proposed research seeks to develop novel algorithms and a simulator to quantummechanically model nanoscale devices in both two and three-dimensions. Algorithms will be developed to model both the dc and ac current response. The focus of the proposed work is on a broad range of nanodevice structures within the effective mass and tight binding approaches. Examples of these structures include devices based on carbon nanotubes, graphene, silicon nanowires and quantum well superlattices.Intellectual merit: The proposed work combines the expertise of co-PIs from electrical engineering and applied mathematics departments to probe the physics of nanodevices accurately and efficiently using the Green?s function approach. The developed algorithms will be applicable to devices in the areas of low power / voltage and energy applications. Further, the approach has the potential for a significant improvement in the simulation speed over current widely used approaches for steady state simulations with decoherence.Broader impacts: The proposed research will involve training of both graduate and undergraduate students in methods to model nanodevices and the study of their underlying device physics. We will make an effort to involve female students and students from underrepresented groups. Both electrical engineering courses on devices and an applied mathematics course on high performance computing will leverage this research to explore nanodevices and algorithms respectively. Outreach to high school students will occur by interactions to illustrate the role of modeling and simulation in discovery and technology development.
目的:提出的研究旨在开发新的算法和模拟器,以在二维和三维上对纳米级器件进行量子力学建模。将开发算法来模拟直流和交流电流响应。所提出的工作的重点是在有效质量和紧密结合方法内广泛的纳米器件结构。这些结构的例子包括基于碳纳米管、石墨烯、硅纳米线和量子阱超晶格的器件。智力优势:这项工作结合了来自电子工程和应用数学系的合作pi的专业知识,利用Green?S函数法。开发的算法将适用于低功率/电压和能源应用领域的设备。此外,与目前广泛使用的具有退相干的稳态模拟方法相比,该方法具有显著提高模拟速度的潜力。更广泛的影响:提议的研究将包括对研究生和本科生在纳米器件建模方法和其潜在器件物理研究方面的培训。我们将努力让女学生和弱势群体的学生参与进来。关于器件的电气工程课程和关于高性能计算的应用数学课程将分别利用这项研究来探索纳米器件和算法。以互动的方式向高中学生展示建模和模拟在发现和技术发展中的作用。

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)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Manjeri Anantram其他文献

Manjeri Anantram的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Manjeri Anantram', 18)}}的其他基金

SemiSynBio: Collaborative Research: DNA-based Electrically Readable Memories
SemiSynBio:合作研究:基于 DNA 的电可读存储器
  • 批准号:
    1807391
  • 财政年份:
    2018
  • 资助金额:
    $ 34万
  • 项目类别:
    Continuing Grant
Mechanically strained silicon nanowire optoelectronic devices
机械应变硅纳米线光电器件
  • 批准号:
    1001174
  • 财政年份:
    2010
  • 资助金额:
    $ 34万
  • 项目类别:
    Standard Grant
CDI-Type I: Modeling Quantum Tunnel Current to Statistically Sequence Biomolecules
CDI-Type I:模拟量子隧道电流以对生物分子进行统计测序
  • 批准号:
    1027812
  • 财政年份:
    2010
  • 资助金额:
    $ 34万
  • 项目类别:
    Standard Grant
Collaborative Research: Multi-Level Behavior, Material Scalability and Energy Efficiency of 1-D Phase-Change Nanostructures
合作研究:一维相变纳米结构的多级行为、材料可扩展性和能源效率
  • 批准号:
    1006182
  • 财政年份:
    2010
  • 资助金额:
    $ 34万
  • 项目类别:
    Continuing Grant

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 34万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了