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首页> 外文期刊>IEEE / ASME Transactions on Mechatronics >Rapid Probe Engagement and Withdrawal With Force Minimization in Atomic Force Microscopy: A Learning-Based Online-Searching Approach
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Rapid Probe Engagement and Withdrawal With Force Minimization in Atomic Force Microscopy: A Learning-Based Online-Searching Approach

机译：在原子力显微镜中的力量最小化快速探测和戒断：基于学习的在线搜索方法

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摘要

In this article, the problem of rapid probe engagement and withdrawal in atomic force microscopy (AFM) is addressed. Probe engagement and withdrawal is needed in almost all AFM operations, ranging from imaging to nanomanipulation. However, due to the highly nonlinear force-distance relation, large probe-sample interaction force can be induced during the probe engagement and withdrawal process, resulting in sample deformation and damage and measurement errors. Rapid probe engagement and withdrawal is needed to achieve high-speed AFM operations, particularly, to capture and interrogate dynamic evolutions of the sample. We propose an online-searching-based optimization approach to minimize both the engagement (and withdrawal) time and the interaction force. The force-displacement profile of the probe is partitioned and then optimized sequentially, by immersing optimal trajectory design and iterative learning control into the Fibonacci search process. The proposed approach is illustrated through experimental implementations on two different types of polymer species, a polydimethylsiloxane sample, and a dental silicone sample, respectively.
机译：在本文中，解决了原子力显微镜（AFM）的快速探针接合和戒断的问题。在几乎所有AFM操作中需要探测啮合和提取，从成像到纳米尺寸。然而，由于高度非线性力 - 距离关系，可以在探针接合和取出过程中引起大的探针样品相互作用力，导致样品变形和损坏和测量误差。需要快速探测和撤回来实现高速AFM操作，特别是捕获和询问样品的动态演进。我们提出了一种基于在线搜索的优化方法，以最大限度地减少接合（和提款）时间和交互力。通过将最佳轨迹设计和迭代学习控制浸入Fibonacci搜索过程，将探针的力 - 位移分布分隔，然后顺序地优化。所提出的方法是通过两种不同类型的聚合物物种，聚二甲基硅氧烷样品和牙硅样品上的实验实施例来说明。

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