Rapid Probe Engagement and Withdrawal With Force Minimization in Atomic Force Microscopy: A Learning-Based Online-Searching Approach
Rapid Probe Engagement and Withdrawal With Force Minimization in Atomic Force Microscopy: A Learning-Based Online-Searching Approach
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DOI：
10.1109/tmech.2020.2971464
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Jingren Wang;Q. Zou
中科院分区：
文献类型：
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作者：
Jingren Wang;Q. Zou
在本文中，在几乎所有的AFM操作中都需要探测探测器（AFM）的快速探测和撤回。关系，在探测过程中可以诱导大探针 - 样本相互作用，从而导致样本变形，损坏和测量误差。高速AFM操作，尤其是捕获样本的动态演变，我们提出了一种基于在线搜索的优化方法，以最大程度地减少参与时间（和撤回）时间和相互作用。通过将最佳轨迹设计和迭代学习控制浸入斐波那契搜索过程中，对探针进行了划分，然后依次优化。分别对两种不同类型的聚合物物种，聚二甲基硅氧烷样品和牙齿硅酮样品进行实施。
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.