PFI-TT: Active Acoustic Noise Cancellation and Control for Scanning Probe Microscopy

PFI-TT：扫描探针显微镜的主动声学噪声消除和控制

• 批准号: 2234449
• 负责人: Qingze Zou
• 金额: $ 25万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234449&HistoricalAwards=false
• 关键词: PFI; TT; Active; Acoustic; Noise

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to create a technology to eliminate image distortions in scanning probe microscopes (SPM). An SPM is an instrument used for studying surfaces at the nanoscale level. The proposed technology is disruptive compared to existing noise isolation approaches as it is based on novel computer algorithms instead of bulky and costly hardware solutions. The success of the project will enable SPMs to attain sub-nanometer accuracy. The proposed solution is crucial for SPM companies to expand their footprints in the semiconductor and chip making industries. The proposed project aims to accomplish the following tasks: (1) achieve acoustic noise cancellation when the signal to noise ratio is low (as in cleanroom nanometrology application), by combining data-driven Kalman and Wiener filtering, sensing enhancement, and iterative optimization and adaptation together; (2) achieve acoustic noise control for in-liquid imaging of live biological species, by capturing and accounting for the hydrodynamic force effects; and (3) integrate the proposed algorithms into the software of commercial SPM systems. The proposed research features a holistic approach combining both online control and offline filtering, a data-driven, dynamics-based framework that eliminates the performance-robustness trade-off, a new SPM imaging mode that is robust to acoustic noise, and a gradient-based coherence minimization methodology that optimizes the performance with little to no need for a priori knowledge of the noise and the SPM system.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.

该创新-技术翻译合作伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是创建一种消除扫描探针显微镜（SPM）中图像失真的技术。SPM是一种用于研究纳米级表面的仪器。与现有的噪声隔离方法相比，所提出的技术具有破坏性，因为它基于新颖的计算机算法，而不是庞大且昂贵的硬件解决方案。该项目的成功将使SPM达到亚纳米精度。拟议的解决方案对于SPM公司扩大其在半导体和芯片制造行业的足迹至关重要。本课题主要完成以下几个方面的工作：（1）实现低信噪比时的声噪声消除（如在洁净室纳米计量学应用中），通过将数据驱动的卡尔曼和维纳滤波、感测增强以及迭代优化和自适应结合在一起;（2）通过捕获和考虑流体动力效应，实现用于活生物物种的液体内成像的声学噪声控制;（3）将所提出的算法集成到商用SPM系统的软件中。拟议的研究采用了一种结合在线控制和离线过滤的整体方法，一种数据驱动的，基于动态的框架，消除了性能-鲁棒性的权衡，一种新的SPM成像模式，对声学噪声具有鲁棒性，和梯度-基于相干性最小化方法，该方法优化了性能，几乎不需要噪声和SPM系统的先验知识。该奖项反映了NSF的法定基金会的使命是履行其使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评价，被认为值得支持。

项目成果

Data-driven robust iterative learning control of linear systems

• DOI: 10.1016/j.automatica.2024.111646
• 发表时间: 2024-06
• 期刊: Automatica
• 影响因子: 6.4
• 作者: Zezhou Zhang;Qingze Zou

Data-Driven Robust Optimal Acoustic Noise Filtering of Atomic Force Microscope Image

数据驱动的原子力显微镜图像鲁棒最优声学噪声过滤

• 发表时间: 2023
• 期刊: IEEE/ASME International Conference on Advanced Intelligent Mechatronics
• 作者: Jiarong Chen;Qingze Zou
• 通讯作者: Qingze Zou