Fractal Compression and Adaptive Sampling With HV Partitioning: Accelerating the Scanning Process in Scanning Probe Microscopy Available to Purchase

Typically, a scanning probe microscope (SPM) moves the scanning tip along a zig-zag trajectory. For a given scanning mechanism, the time needed to image an area depends mainly on the number of samples and the size of the image. The imaging speed is further compromised by drifts associated with the substrate and the piezo scanner. It is therefore desirable to improve the imaging speed with limited impact to the effective resolution of the resulting image. In this paper, an adaptive sampling algorithm based on the fractal compression theory of iterated transformations is proposed to address the tradeoff between scanning time and resolution. Instead of scanning a substrate pixel by pixel in an ordered way, the proposed adaptive algorithm starts with a coarse mode scan, measuring only the registered positions within blocks of determined size. Registered positions are defined to constitute the four corners of a specified block. A unique feature of the proposed algorithm is that if the differences among the four registered positions are greater than a preset threshold, the scan process will automatically switch from a coarse mode scan to a zoom-in mode scan. The original block is then divided into two subblocks with HV partitioning method. In the zoom-in mode scan, the microscope uses a finer resolution to scan one of two subblocks. Each sub-block is then treated as another specified block, and the process of coarse mode scanning is repeated until the desired image is synthesized. The main characteristic of this approach is based on the assumption that image redundancy can be efficiently exploited on a block-wise basis. This procedure is particularly relevant to SPMs designed around closed-loop feedback scanners which are gaining in popularity because of their ability to eliminate drift and hysterisis. By applying an adaptive sampling scheme, we have demonstrated that the number of the required samples can be significantly reduced and the scanning process can be accelerated with minimal impact to the image quality.