Multiple-Energy-Assisted Ultrasharp Probe-Based Nanomanufacturing for High-Resolution and High-Efficiency Nanopatterning
基于多能量辅助 Ultrasharp 探针的纳米制造,用于高分辨率和高效纳米图案化
基本信息
- 批准号:2006127
- 负责人:
- 金额:$ 60.94万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-07-01 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Nanomanufacturing is the manufacture of sub-100 nanometer patterns, components, devices, and systems. Over the past few decades, nanomanufacturing progress has greatly advanced many fundamental research fields from physics to biology and has generated numerous commercial applications, such as biomedical products, semiconductor components, and energy devices. However, high-resolution manufacturing of sub-10 nanometer feature sizes remains a scientific challenge. This award supports fundamental research to create and understand ultrasharp probe-based nanomanufacturing processes for high-resolution and high-efficiency nanopatterning. By using strong and thin carbon nanotubes as patterning tools, this new ultrasharp probe-based nanomanufacturing paradigm will enable high-efficiency manufacturing down to sub-10 nm level and will accelerate innovations in high-resolution flexible and scalable manufacturing. This research will enable a number of science and engineering research and applications, will impact a number of industries, and will help boost the US economy. This research will provide scientific training and research experience to graduate and undergraduate students, particularly women and minorities, from various outreach programs at Binghamton University. Research results will be incorporated into existing advanced manufacturing and nanotechnology courses and will be disseminated through journal and conference publications and through outreach programs to local K-12 students.The goal of this project is to create and understand an ultrasharp probe-based nanomanufacturing technique for high-resolution and high-efficiency nanopatterning down to sub-10 nanometer feature level. This research will integrate electrical field, Joule heating, and mechanical vibration with an atomic force microscope to create an efficient nanomanufacturing platform to overcome the barriers of existing maskless nanomanufacturing techniques. The research team will manufacture machining tools from ultrasharp carbon nanotube atomic force microscope probes, with customized nanotube lengths and orientations, by using an electron microscopy nanomechanical single-nanotube pull-out technique. The team will experimentally characterize the patterning resolution, the manufacturing efficiency, and the tool lifetime. To understand the mechanism of the manufacturing technique, the research team will simulate electric flux density to uncover how the manufacturing parameters, such as the applied voltage and the thickness of resists, govern the resolution of the manufacturing process. Statistical and semi-empirical models will be built to unveil the relationships between the input parameters and the nanomanufacturing performance. This research will also combine the high-resolution manufacturing process with soft lithography to enable flexible and scalable high-resolution nanomanufacturing. It is envisioned that this research will enable a number of new technologies for biomedical, electronic, and energy applications in the research, industrial and governmental sectors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
纳米制造是指 100 纳米以下图案、组件、设备和系统的制造。在过去的几十年里,纳米制造的进步极大地推进了从物理学到生物学的许多基础研究领域,并产生了大量的商业应用,例如生物医学产品、半导体元件和能源设备。然而,亚 10 纳米特征尺寸的高分辨率制造仍然是一个科学挑战。该奖项支持基础研究,以创建和理解基于超锐探针的纳米制造工艺,以实现高分辨率和高效率的纳米图案化。通过使用坚固而薄的碳纳米管作为图案化工具,这种基于超锐探针的新型纳米制造范式将实现低至 10 纳米以下水平的高效率制造,并将加速高分辨率灵活和可扩展制造的创新。这项研究将使许多科学和工程研究和应用成为可能,将影响许多行业,并将有助于提振美国经济。这项研究将为宾厄姆顿大学各种外展项目的研究生和本科生,特别是女性和少数族裔提供科学培训和研究经验。研究成果将纳入现有的先进制造和纳米技术课程,并将通过期刊和会议出版物以及向当地 K-12 学生的推广计划进行传播。该项目的目标是创建和理解一种基于超锐探针的纳米制造技术低至亚 10 纳米特征水平的高分辨率和高效率纳米图案化。这项研究将电场、焦耳热和机械振动与原子力显微镜相结合,创建一个高效的纳米制造平台,克服现有无掩模纳米制造技术的障碍。研究团队将通过使用电子显微镜纳米机械单纳米管拉出技术,用超锋利的碳纳米管原子力显微镜探针制造加工工具,具有定制的纳米管长度和方向。该团队将通过实验来表征图案分辨率、制造效率和工具寿命。为了了解制造技术的机制,研究团队将模拟电通量密度,以揭示制造参数(例如施加的电压和抗蚀剂厚度)如何控制制造过程的分辨率。将建立统计和半经验模型来揭示输入参数与纳米制造性能之间的关系。这项研究还将高分辨率制造工艺与软光刻相结合,以实现灵活且可扩展的高分辨率纳米制造。预计这项研究将为研究、工业和政府部门的生物医学、电子和能源应用带来许多新技术。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力价值进行评估,被认为值得支持以及更广泛的影响审查标准。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A sensor-based monitoring approach to predict surface profile of vibration-assisted atomic force microscopy (AFM)-based nanofabrication
- DOI:10.1016/j.mfglet.2023.08.109
- 发表时间:2023-08
- 期刊:
- 影响因子:3.9
- 作者:Xinchen Wang;Huimin Zhou;Jia Deng;Zimo Wang
- 通讯作者:Xinchen Wang;Huimin Zhou;Jia Deng;Zimo Wang
Electric-Field and Mechanical Vibration-Assisted Atomic Force Microscope-Based Nanopatterning
基于电场和机械振动辅助原子力显微镜的纳米图案化
- DOI:10.1115/1.4056731
- 发表时间:2022
- 期刊:
- 影响因子:1
- 作者:Zhou, Huimin;Jiang, Yingchun;Ke, Changhong;Deng, Jia
- 通讯作者:Deng, Jia
Sliding energy landscape governs interfacial failure of nanotube-reinforced ceramic nanocomposites
- DOI:10.1016/j.scriptamat.2021.114413
- 发表时间:2022
- 期刊:
- 影响因子:6
- 作者:Ning Li;Christopher M. Dmuchowski;Ying Jiang;Chenglin Yi;Feilin Gou;J. Deng;C. Ke;H. Chew
- 通讯作者:Ning Li;Christopher M. Dmuchowski;Ying Jiang;Chenglin Yi;Feilin Gou;J. Deng;C. Ke;H. Chew
Electric-Field-Assisted Contact Mode AFM-Based Nanolithography with Low Stiffness Conductive Probes
具有低刚度导电探针的基于电场辅助接触模式 AFM 的纳米光刻
- DOI:10.1115/1.4054316
- 发表时间:2022
- 期刊:
- 影响因子:1
- 作者:Zhou, Huimin;Jiang, Yingchun;Dmuchowski, Christopher M;Ke, Changhong;Deng, Jia
- 通讯作者:Deng, Jia
The interplay of intra- and inter-layer interactions in bending rigidity of ultrathin 2D materials
- DOI:10.1063/5.0146065
- 发表时间:2023-04
- 期刊:
- 影响因子:4
- 作者:
- 通讯作者:
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Jia Deng其他文献
Detection and Analysis of Commonly Used Infection Indicators in Patients with Acute Urticaria
急性荨麻疹患者常用感染指标的检测与分析
- DOI:
10.1159/000536642 - 发表时间:
2024 - 期刊:
- 影响因子:2.8
- 作者:
Jia Deng;Jifeng Liu;W. Xiang - 通讯作者:
W. Xiang
Fast dechlorination of trichloroethylene by a bimetallic Fe(OH)2/Ni composite
双金属 Fe(OH)2/Ni 复合材料快速脱氯三氯乙烯
- DOI:
10.1016/j.seppur.2021.119597 - 发表时间:
2022 - 期刊:
- 影响因子:8.6
- 作者:
Jia Deng;Xiang Zhan;Feng Wu;Shuxian Gao;Li-Zhi Huang - 通讯作者:
Li-Zhi Huang
Development of In Vivo Predictive pH-Gradient Biphasic Dissolution Test for Weakly Basic Drugs: Optimization by Orthogonal Design
弱碱性药物体内预测 pH 梯度双相溶出测试的开发:正交设计优化
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0.6
- 作者:
Xiao;Shengying Shi;Junlin He;Jia Deng;Jingou Ji - 通讯作者:
Jingou Ji
Representativity and applicability of small amounts of sediment samples in the determination of absolute diatom abundances (ADA): Evidence from an orbital shaking method
少量沉积物样品在确定硅藻绝对丰度(ADA)中的代表性和适用性:来自轨道振荡法的证据
- DOI:
10.1016/j.marmicro.2020.101851 - 发表时间:
2020-04 - 期刊:
- 影响因子:1.9
- 作者:
Yuanhui Huang;Jian Ren;Jia Deng;Xuefa Shi;Limin Hu;Jianjun Zou;Yanguang Liu;Xiaobing Ma - 通讯作者:
Xiaobing Ma
Solar vaporizing desalination by heat concentration
通过热量集中的太阳能汽化海水淡化
- DOI:
10.1016/j.renene.2020.02.105 - 发表时间:
2020-07 - 期刊:
- 影响因子:8.7
- 作者:
Jingyang Han;Xu Ji;Haiyang Xu;Yuanyuan Heng;Cong Wang;Jia Deng - 通讯作者:
Jia Deng
Jia Deng的其他文献
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{{ truncateString('Jia Deng', 18)}}的其他基金
SLES: Vision-Based Maximally-Symbolic Safety Supervisor with Graceful Degradation and Procedural Validation
SLES:基于视觉的最大符号安全监控器,具有优雅的降级和程序验证功能
- 批准号:
2331763 - 财政年份:2023
- 资助金额:
$ 60.94万 - 项目类别:
Standard Grant
CAREER: Toward Video2Sim: Turning Real World Videos into Simulations
职业:走向Video2Sim:将现实世界的视频变成模拟
- 批准号:
1942981 - 财政年份:2020
- 资助金额:
$ 60.94万 - 项目类别:
Continuing Grant
RI: Small: Inverse Rendering by Co-Evolutionary Learning
RI:小:通过共同进化学习进行逆向渲染
- 批准号:
1854435 - 财政年份:2018
- 资助金额:
$ 60.94万 - 项目类别:
Continuing Grant
BIGDATA: F: Collaborative Research: From Visual Data to Visual Understanding
BIGDATA:F:协作研究:从视觉数据到视觉理解
- 批准号:
1903222 - 财政年份:2018
- 资助金额:
$ 60.94万 - 项目类别:
Standard Grant
BIGDATA: F: Collaborative Research: From Visual Data to Visual Understanding
BIGDATA:F:协作研究:从视觉数据到视觉理解
- 批准号:
1633157 - 财政年份:2016
- 资助金额:
$ 60.94万 - 项目类别:
Standard Grant
RI: Small: Inverse Rendering by Co-Evolutionary Learning
RI:小:通过共同进化学习进行逆向渲染
- 批准号:
1617767 - 财政年份:2016
- 资助金额:
$ 60.94万 - 项目类别:
Continuing Grant
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