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

这项创新技术转化伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是创造一种消除扫描探针显微镜（SPM）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，该技术具有颠覆性，因为它基于新颖的计算机算法，而不是庞大而昂贵的硬件解决方案。该项目的成功将使SPMs达到亚纳米精度。该解决方案对于SPM公司在半导体和芯片制造行业扩大其足迹至关重要。本课题主要完成以下任务：(1)将数据驱动的卡尔曼滤波和维纳滤波、传感增强、迭代优化与自适应相结合，在信噪比较低的情况下（如洁净室纳米测量应用中）实现降噪；(2)通过捕获和计算水动力效应，实现对活体生物在液成像的噪声控制；(3)将提出的算法集成到商用SPM系统软件中。该研究提出了一种结合在线控制和离线滤波的整体方法，一种数据驱动的基于动态的框架，消除了性能-鲁棒性权衡，一种新的SPM成像模式，对噪声具有鲁棒性，以及一种基于梯度的相干最小化方法，该方法优化了性能，几乎不需要先验的噪声和SPM系统知识。该奖项反映了美国国家科学基金会的法定使命，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估，被认为值得支持。

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.