A Wavelet-Based AFM Fast Imaging Method With Self-Tuning Scanning Frequency

Due to its numerous advantages, atomic force microscopy (AFM) has been widely utilized in various fields, such as nanotechnology, nanomanipulation, bioscience, etc. However, when considering the increasing requirements from different applications, low scanning speed is one of the most challenging drawbacks which prevents further applications of an AFM system. Based on this observation, this paper proposes a novel wavelet-based AFM fast imaging method with an intelligent adjustment mechanism for scanning frequency over different parts of detected samples, which is especially efficient when scanning biological samples with sparse features. More specifically, a sample is first skipped through quickly with forward scan, during which wavelet analysis is implemented for the collected data to distinguish interesting areas from uninteresting ones, based on which, the sample is then reversely scanned with varying frequencies to obtain trusty information to generate an accurate image for its surface. That is, the proposed fast AFM imaging method carefully scans the identified interesting areas at comparatively low frequency, while skipping through the uninteresting ones quickly with much higher speed, so as to reduce scanning time and simultaneously enhance imaging performance. Performance analysis demonstrates that, due to its unique ability of intelligent tuning for scanning speeds, the designed wavelet-based AFM scanning method maximally increases the imaging speed by ten times. In addition, both simulation and experimental results also verify the good performance of the proposed method. The designed imaging method is finally utilized to scan some biological samples, with collected results further exhibiting its promising prospect.