课题基金 / 基金详情

Big-data for nano-electronics

Big-data for nano-electronics
纳米电子学大数据
批准号:
MR/T021519/1
负责人:
Patrick Parkinson
金额:
$135.11万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2020
资助国家:
英国
项目状态:
未结题
起止时间:
2020 至 --
关键词:

项目摘要

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中文摘要
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英文摘要
Demand for high density, integrated electronics has become a defining feature of modern technology. At its ultimate limit, nanotechnology can enable low-cost and highly scalable sensors, computing elements, and lighting. The industrial benefits are clear - in particular bottom-up fabrication allows for high-level functionality and huge production scale at low cost. As this production technique emerges from the laboratory and into industry, issues such as yield, heterogeneity, and functional parameter spread have emerged as a critical aspect for efficacy to be established in advanced nanomaterials.To date, no framework exists for studying inhomogeneity in functional nano-electronics. I will combine highly-scaled measurements with cutting-edge data techniques to establish a gold-standard methodology for functional nanotechnology development, enabling industrial take-up. This will build on experimental approaches that I have recently demonstrated, including machine-vision identification of nanomaterials and automated electronic and optical spectroscopy, alongside computational approaches for rapid and technique-independent re-identification of single nanoparticles. I will implement analytics which draw on existing population-study methods such as linear and multivariate correlation; a specific goal of this project is to translate advanced techniques from diverse fields including astrophysics and health research, and in particular apply Bayesian analysis for model identification and augmented intelligence (including machine learning methods) where appropriate. These methodologies will be developed to study cutting edge challenges in functional nanomaterials; starting with the development of lasers for chip-to-chip communication, and the production of an industrially relevant capability for single-particle nanotechnology characterisation. By bringing this methodology together with pick-and-place capability through project partners, this project will enable demonstration of extremely low-yield yet transformative devices based on novel nanotechnology, for sensing, telecommunication or quantum devices.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1021/acsphotonics.3c00355
发表时间: 2023-08-16
期刊: ACS PHOTONICS
影响因子: 7
作者: [Barrett, R. M., McMahon, J. M., Ahumada-Lazo, R., Alanis, J. A., Parkinson, P., Schulz, S., Kappers, M. J., Oliver, R. A., Binks, D.]
通讯作者: Binks, D.
DOI: 10.1021/acsnano.2c01086
发表时间: 2022-06-28
期刊: ACS NANO
影响因子: 17.1
作者: [Church, Stephen A., Choi, Hoyeon, Al-Amairi, Nawal, Al-Abri, Ruqaiya, Sanders, Ella, Oksenberg, Eitan, Joselevich, Ernesto, Parkinson, Patrick W.]
通讯作者: Parkinson, Patrick W.
DOI: 10.1021/acs.jpcc.1c03680
发表时间: 2021-07-08
期刊: The journal of physical chemistry. C, Nanomaterials and interfaces
影响因子: --
作者: [Boras G, Yu X, Fonseka HA, Davis G, Velichko AV, Gott JA, Zeng H, Wu S, Parkinson P, Xu X, Mowbray D, Sanchez AM, Liu H]
通讯作者: Liu H
DOI: 10.3389/fchem.2020.607481
发表时间: 2020
期刊: Frontiers in chemistry
影响因子: 5.5
作者: [Jiang N, Joyce HJ, Parkinson P, Wong-Leung J, Tan HH, Jagadish C]
通讯作者: Jagadish C
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
复杂数据下半参数转换模型及其在老年慢性病发展中的应用研究
  • 批准号:
    72101261
  • 项目类别:
    青年科学基金项目(C类)
  • 资助金额:
    30.0万元
  • 批准年份:
    2021
  • 负责人:
    孙韬
  • 依托单位:
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位: