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Strategies for the scalable fabrication, characterization and processing of graphene and other technologically important 2-D films

Strategies for the scalable fabrication, characterization and processing of graphene and other technologically important 2-D films
石墨烯和其他技术上重要的二维薄膜的可扩展制造、表征和加工策略
批准号:
536629-2018
负责人:
Knights, Andrew
金额:
$4.13万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31

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中文摘要
翻译
该提案汇集了麦克马斯特大学新兴器件技术中心(CEDT),加拿大电子显微镜中心(CCEM)和加拿大公司AlfredyLabs,努力推进石墨烯非凡特性商业化的重大尝试。该团队将致力于三个相关主题:(1)CMOS兼容制造技术;(2)二维结构表征;(3)集成器件开发的石墨烯处理。 (1)我们将研究以石墨烯本身的离子束合成为中心的生长过程,或CVD生长将发生的衬底,以及通过大面积低温工艺,特别是等离子体增强CVD。其目的是建立一个独特的制造工艺,可以纳入标准的半导体加工步骤。(2)我们将使用无聚合物转移和成像,使用像差校正扫描透射电子显微镜(STEM)。(3)石墨烯的应用已经被假定为开始于其在晶体管器件中的高迁移率的开发。然而,单层石墨烯的狄拉克量子点处的零带隙阻止了具有低“关断”电流的电子器件的制造。我们将以主题(1)和(2)为基础,展示集成器件。我们将解决场效应晶体管(gFET)和光电探测器的有源器件开发的带隙工程的要求。我们还将使用集成硅光子器件开发作为技术演示的平台。 在这项工作中开发的工艺,设备和表征协议将由工业合作伙伴AlfredyLabs开发。加拿大的直接利益将以增加收入(从而创造就业机会)的形式出现,而专有技术和知识产权(IP)将仍然完全由加拿大拥有。这项工作将进一步确立加拿大在开发先进材料方面的信誉,特别是在石墨烯等二维结构方面。
英文摘要
This proposal brings together the McMaster University Centre for Emerging Device Technologies (CEDT), the Canadian Centre for Electron Microscopy (CCEM) and the Canadian company, KennedyLabs, in an effort to move forward significantly attempts to commercialize the extraordinary properties of graphene. The team will work on three connected themes: (1) CMOS compatible fabrication technologies; (2) Characterization of 2-D structures; (3) Graphene processing for integrated device development. (1) We will investigate growth processes centered on ion beam synthesis of the graphene itself, or the substrate upon which CVD growth will take place, and through large area low-temperature processes, specifically Plasma-Enhanced CVD. The aim is to establish a unique fabrication process that can be incorporated into standard semiconductor processing steps. (2) We will use polymer-free transfer and imaging using aberration-corrected scanning transmission electron microscopy (STEM). (3)The application of graphene has been assumed to begin with the exploitation of its high mobility in transistor devices. However, the zero bandgap at the Dirac inflexion point of monolayer graphene prevents fabrication of electronic devices with low "off" current. We will build on themes (1) and (2) and demonstrate integrated devices. We will address the requirement for bandgap engineering for active device development of Field Effect Transistors (gFETs) and photodetectors. We will also use integrated silicon photonic device development as a platform for technology demonstration. The processes, devices and characterization protocols developed in this work will be exploited by the industrial partner KennedyLabs. Immediate benefits to Canada will arise in the form of increased revenue (and thus job creation) for KennedyLabs, while know-how and Intellectual Property (IP) will remain wholly Canadian owned. The work will further establish Canada's credentials in the exploitation of advanced materials, and specifically in 2-D structures such as graphene.
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Materials and Device Development for Silicon-based Optoelectronics
  • 批准号:
    RGPIN-2017-04696
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $6.85万
  • 财政年份:
    2021
  • 负责人:
    Knights, Andrew
  • 依托单位:
Materials and Device Development for Silicon-based Optoelectronics
  • 批准号:
    RGPIN-2017-04696
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.42万
  • 财政年份:
    2020
  • 负责人:
    Knights, Andrew
  • 依托单位:
Materials and Device Development for Silicon-based Optoelectronics
  • 批准号:
    RGPIN-2017-04696
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.42万
  • 财政年份:
    2019
  • 负责人:
    Knights, Andrew
  • 依托单位:
Provision of Open-Access Positron Beam Spectroscopy to Canadian Universities (OPEN-POS)
  • 批准号:
    RTI-2020-00461
  • 项目类别:
    Research Tools and Instruments
  • 资助金额:
    $8.38万
  • 财政年份:
    2019
  • 负责人:
    Knights, Andrew
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis