Accurate and rapid modelling of AFM tip morphology through scanning sphere nanoparticles

The tip shape is widely estimated from the images of an unknown sample surface with protruding features by using blind modelling algorithm. However, its estimated results are readily disturbed by the noise suppression parameters in the algorithm and the tip is apt to be contaminated by the sample surface features. To solve these problems, this paper proposes an accurate and rapid simulation model to estimate tip morphology. We choose regular spherical nanoparticles as the sample, and calculate their diameter by using the least squares method. The nanoparticle morphology is calculated as 'the tip' to erode the image for estimating the tip morphology. The effectiveness of this proposed method is demonstrated via the nanoparticle morphology reconstruction experiment through comparing nanoparticle reconstructed image with its actual morphology. It is proved that the proposed method not only improves image quality and measurement precision but also saves time.