TY - JOUR
T1 - Rapid Probe Engagement and Withdrawal with Force Minimization in Atomic Force Microscopy
T2 - A Learning-Based Online-Searching Approach
AU - Wang, Jingren
AU - Zou, Qingze
N1 - Funding Information: Manuscript received July 7, 2019; revised November 6, 2019 and January 27, 2020; accepted January 28, 2020. Date of publication February 4, 2020; date of current version April 15, 2020. Recommended by Technical Editor I.-M. Chen. This work was supported by the NSF under Grants IDBR-1353890, CMMI 1663055, and 1851907. (Corresponding author: Qingze Zou.) The authors are with the Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, Piscat-away, NJ 08854 USA (e-mail: jw986@scarletmail.rutgers.edu; qzzou@ soe.rutgers.edu). Publisher Copyright: © 1996-2012 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
KW - Fibonacci search
KW - high-speed atomic force microscopy
KW - iterative learning control
KW - real-time optimization
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U2 - https://doi.org/10.1109/TMECH.2020.2971464
DO - https://doi.org/10.1109/TMECH.2020.2971464
M3 - Article
VL - 25
SP - 581
EP - 593
JO - IEEE/ASME Transactions on Mechatronics
JF - IEEE/ASME Transactions on Mechatronics
SN - 1083-4435
IS - 2
M1 - 8981910
ER -