Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7986243 | Micron | 2017 | 17 Pages |
Abstract
Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance.
Related Topics
Physical Sciences and Engineering
Materials Science
Materials Science (General)
Authors
Hui Liu, Yingzi Li, Yingxu Zhang, Yifu Chen, Zihang Song, Zhenyu Wang, Suoxin Zhang, Jianqiang Qian,