Adaptive Scan for Atomic Force Microscopy Based on Online Optimization: Theory and Experiment
| Autor: | Kaixiang Wang, Dragan Nesic, Michael G. Ruppert, Yuen Kuan Yong, Chris Manzie |
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| Rok vydání: | 2020 |
| Předmět: |
Horizontal scan rate
0209 industrial biotechnology Computer science Image quality Atomic force microscopy 02 engineering and technology 021001 nanoscience & nanotechnology Scan line Contact force Constant linear velocity 020901 industrial engineering & automation Exponential stability Control and Systems Engineering Electrical and Electronic Engineering 0210 nano-technology Performance metric Algorithm |
| Zdroj: | IEEE Transactions on Control Systems Technology. 28:869-883 |
| ISSN: | 2374-0159 1063-6536 |
| DOI: | 10.1109/tcst.2019.2895798 |
| Popis: | A major challenge in atomic force microscopy is to reduce the scan duration while retaining the image quality. Conventionally, the scan rate is restricted to a sufficiently small value in order to ensure a desirable image quality as well as a safe tip–sample contact force. This usually results in a conservative scan rate for samples that have a large variation in aspect ratio and/or for scan patterns that have a varying linear velocity. In this paper, an adaptive scan scheme is proposed to alleviate this problem. A scan line-based performance metric balancing both imaging speed and accuracy is proposed, and the scan rate is adapted such that the metric is optimized online in the presence of aspect ratio and/or linear velocity variations. The online optimization is achieved using an extremum-seeking approach, and a semiglobal practical asymptotic stability result is shown for the overall system. Finally, the proposed scheme is demonstrated via both simulation and experiment. |
| Databáze: | OpenAIRE |
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