CIF: Small: Collaborative Research: Signal processing for enabling high speed probe based nanoimaging

纳米科学和纳米技术的一个关键推动者是原子力显微镜(AFM)，它开辟了在原子尺度上询问和操纵物质的新领域。它在理解亚原子分子结构、蛋白质折叠动力学和材料表征方面取得了突破。更快的基于原子力显微镜的成像的潜在影响是巨大的，例如，它将使人们能够在纳米尺度上研究迄今无法获得的材料动力学。这项研究的目的是研究现代信号处理技术，使成像速度提高一个数量级。研究人员将通过学生访问和专门研讨会，推动工程学和AFM社区之间技术诀窍的协同转移。这些发现将在适当的水平上整合到本科生和研究生的课程中。AFM的主要部件是一个悬臂，它在皮牛顿尺度的力的作用下发生偏转。这项研究的重点是动态模式AFM操作，其中悬臂轻轻地轻拍被成像的样品；生物样品成像的选择模式。尽管力确实形成了样本地形的良好指示器，但系统内存和AFM系统动力学的非线性排除了将悬臂力直接映射到更精细的地形描述中的可能性，特别是在高成像速度下。研究人员以一种数学上易于处理的方式对复杂的纳米级相互作用进行建模，这有助于开发样本特征的最大后验概率(MAP)序列检测。将研究因子图表示法和适当推理方案的开发。算法将在现场可编程门阵列上实现，并用实验数据进行详尽的测试。

A key enabler of Nanoscience and Nanotechnology is the Atomic Force Microscope (AFM) that has opened up new realms, of interrogation and manipulation of matter at the atomic scale. It has resulted in breakthroughs in understanding sub-atomic molecular structure, protein folding dynamics and materials characterization. The potential impact of significantly faster AFM based imaging is immense, e.g., it will allow the study of dynamics of material at the nanoscale that was hitherto not accessible. The aim of this research is to study modern signal processing techniques that achieve gains in imaging speeds by an order of magnitude. The investigators will push the synergistic transfer of knowhow between the engineering and the AFM communities through student visits and specialized workshops. The findings will be integrated at appropriate levels into the undergraduate and graduate curriculum.The main component of an AFM is a cantilever that deflects in response to forces at the pico-Newton scale. The focus of this research is the dynamic mode AFM operation, where the cantilever gently taps the sample being imaged; the mode of choice for imaging biological samples. Even though forces do form a good indicator of sample topography, the system memory and the nonlinearities of the AFM system dynamics preclude a direct mapping of the cantilever forces into a finer description of topography, especially at high imaging speeds. The investigators model the complex nanoscale interactions in a mathematically tractable manner that facilitates the development of maximum a posteriori (MAP) sequence detection of the sample features. Factor graph representations and the development of appropriate inference schemes will be studied. The algorithms will be implemented on FPGAs and tested exhaustively with experimental data.