Postprocessing Algorithm for Driving Conventional Scanning Tunneling Microscope at Fast Scan Rates

We present an image postprocessing framework for Scanning Tunneling Microscope (STM) to reduce the strong spuriousoscillations and scan line noise at fast scan rates and preserve the features, allowing an order of magnitude increase in the scanrate without upgrading the hardware. The proposed method consists of two steps for large scale images and four steps for atomicscale images. For large scale images, we first apply for each line an image registration method to align the forward and backwardscans of the same line. In the second step we apply a “rubber band” model which is solved by a novel Constrained Adaptive andIterative Filtering Algorithm (CIAFA). The numerical results on measurement from copper(111) surface indicate the processedimages are comparable in accuracy to data obtained with a slow scan rate, but are free of the scan drift error commonly seen inslow scan data. For atomic scale images, an additional first step toremove line-by-line strong background fluctuations and a fourthstep of replacing the postprocessed image by its ranking map as the final atomic resolution image are required. The resulting imagerestores the lattice image that is nearly undetectable in the original fast scan data.