基于数学形态学方法的AFM探针建模研究

AFM tip estimation based on mathematical morphology

AFM扫描图像可被认为是探针针尖的形貌和扫描样品表面形貌的数学形态学卷积结果,需要用反卷积的方法排除扫描图像中探针形貌引起的失真影响。本文在已有基于数学形态学的探针盲建模算法基础上,提出了一种可快速实现特征点优化提取的方法,同时提出了一种可降低最优降噪阈值估计复杂性的基于临界阈值搜索新方法。最后给出了仿真与CNT扫描图像的重构实验结果。实验表明,本文介绍的方法提高了探针建模的计算速度和建模精度,可以对AFM成像质量进行有效的失真修正和改善。

Atomic force microscope （AFM） images can be considered as the convolution of the geometry of the sample and the shape of the tip. Therefore the geometry of the tip should be known for eliminating the contributions of the tip shape in the scanning image using disconvolution. Blind tip evaluation based on mathematical morphology is used widely in the existing methods for estimating the tip shape. A new blind tip estimation method is proposed to quickly extract the feature points in the image, which can save calculation time and determine the optimal noise threshold based on the critical threshold. This paper demonstrates the improvement of the new method on speed and precision. Simulation and reconstruction experiment of carbon nano-tube （CNT） scanning image are given. Experiment results indicate that the proposed method improves the calculation speed and modeling precision of tip estimation.