RESUMO
Quantitative determination of the geometry of an atomic force microscope (AFM) probe tip is critical for robust measurements of the nanoscale properties of surfaces, including accurate measurement of sample features and quantification of tribological characteristics. Blind tip reconstruction, which determines tip shape from an AFM image scan without knowledge of tip or sample shape, was established most notably by Villarrubia [J. Res. Natl. Inst. Stand. Tech. 102 (1997)] and has been further developed since that time. Nevertheless, the implementation of blind tip reconstruction for the general user to produce reliable and consistent estimates of tip shape has been hindered due to ambiguity about how to choose the key input parameters, such as tip matrix size and threshold value, which strongly impact the results of the tip reconstruction. These key parameters are investigated here via Villarrubia's blind tip reconstruction algorithms in which we have added the capability for users to systematically vary the key tip reconstruction parameters, evaluate the set of possible tip reconstructions, and determine the optimal tip reconstruction for a given sample. We demonstrate the capabilities of these algorithms through analysis of a set of simulated AFM images and provide practical guidelines for users of the blind tip reconstruction method. We present a reliable method to choose the threshold parameter corresponding to an optimal reconstructed tip shape for a given image. Specifically, we show that the trend in how the reconstructed tip shape varies with threshold number is so regular that the optimal, or Goldilocks, threshold value corresponds with the peak in the derivative of the RMS difference with respect to the zero threshold curve vs. threshold number.
