Intelligent tracking control system for fast image scanning of atomic force microscopes

Sajal K. Das*, Hemanshu R. Pota, Ian R. Petersen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Citations (Scopus)


Atomic force microscope (AFM) is a type of scanning probe microscopy technique which is used to measure the characteristics of various specimens at an atomic level through surface imaging. In the imaging process of the AFM the sample is placed on a positioning unit termed as nanopositioner. The performance of the AFM for fast image scanning is limited to the one percent of the first resonance frequency of its positioning unit. Many imaging applications require a faster response and high quality imaging than what can be achieved using the currently available commercial AFMs. The need for high speed imaging is the reduction of the computational time to capture an image. The time require to capture an image of a reference grating sample for an 8 μm × 8 μm area and 256 number of scan lines at the scanning rate of 1 Hz and 125 Hz are 170s and 2 s. This shows the importance of the increase of scan frequency in terms of operation time. The tracking performance of the nanopositioner of the AFM for high speed imaging is limited due to the vibration of the nanopositioner, cross coupling effect between the axes of the nanopositioner and nonlinear effects in the form of hysteresis and creep. In this chapter we have proposed an intelligent multi-variable tracking controller to compensate the effect of vibration, cross coupling and nonlinearities in the form of hysteresis and creep in AFM for fast image scanning. Experimental results in time and frequency domain are presented to show the effectiveness of the proposed controller.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages41
Publication statusPublished - 2015
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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