动态原子力显微镜针尖-样品力的近实时提取框架（dAFM）
A near real-time framework for extracting tip-sample forces in dynamic atomic force microscopy (dAFM)
摘要：
原子力显微镜（AFM）是一种多功能、高分辨率的工具，用于在纳米尺度上表征各种样品的形貌和材料特性。微悬臂梁尖端与试样的相互作用导致悬臂梁偏转，该悬臂梁偏转由光学传感机构测量，随后用于构建样品形貌。近年来，人们越来越关注使用AFM来表征凝胶和活细胞等软样品。由于尖端-样品相互作用力的复杂性和竞争性，这仍然具有挑战性-需要大的尖端-样品相互作用力来实现良好的信噪比;然而，大的作用力往往会使软样品变形和破坏。在原位估计的局部针尖样品的相互作用力是必要的，以控制AFM悬臂梁运动，并防止破坏的软样品，同时保持良好的信噪比。这就需要能够快速估计操作期间悬臂偏转的尖端-样品力。本文提出了第一种方法的近实时框架的尖端样品力反演。我们提出的反问题提取针尖样品力作为一个无约束的优化问题。利用图形处理单元（GPU）开发了一种快速、并行的正演求解器。这个正向求解器显示了一个有效的30000倍的速度比可比的CPU实现，在毫秒的计算时间。该正向求解器与基于GPU的粒子群优化实现相结合。我们说明了三个类的尖样品相互作用反演的框架。这些反转中的每一个都是在亚秒的时间内进行的，显示出与在线AFM成像和材料表征相结合的潜力
英文摘要：
The atomic force microscope (AFM) is a versatile, high-resolution tool used to characterize the topography and material properties of a large variety of specimens at nano-scale. The interaction of the micro-cantilever tip with the specimen causes cantilever deflections that are measured by an optical sensing mechanism and subsequently utilized to construct the sample topography. Recent years have seen increased interest in using the AFM to characterize soft specimens like gels and live cells. This remains challenging due to the complex and competing nature of tip-sample interaction forces - large tip-sample interaction force is necessary to achieve good signal-to-noise ratios; However, large force tends to deform and destroy soft samples. In situ estimation of the local tip-sample interaction force is needed to control the AFM cantilever motion and prevent destruction of soft samples while maintaining a good signal-to-noise ratio. This necessitates the ability to rapidly estimate the tip-sample forces from the cantilever deflection during operation. This paper proposes a first approach to a near real-time framework for tip-sample force inversion. We pose the inverse problem of extracting the tip-sample force as an unconstrained optimization problem. A fast, parallel forward solver is developed by utilizing graphical processing units (GPU). This forward solver shows an effective 30000 fold speed-up over a comparable CPU implementation, resulting in milli-second calculation times. The forward solver is coupled with a GPU based particle-swarm optimization implementation. We illustrate the framework on three classes of tip-sample interaction inversions. Each of these inversions is performed in sub-second timings, showing potential for integration with on-line AFM imaging and material characterization
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
