Non-equidistant scanning path generation for the evaluation of surface curvature in metrological scanning probe microscopes

In this paper, we present a novel geometry information-based adaptive step (non-equidistance) scanning path generation method for metrological scanning probe microscopes. This method reduces the total amount of required data and enables faster surface scanning speed for large industrial workpieces while preserving adequate geometric information for performance evaluation after surface reconstruction. The grid points are generated iteratively while gaining knowledge of the surface geometry step by step. We focus on the curvature properties and then propose a metric for the curvature information based on the triangulated surface geometry. With certain convergence criteria on the curvature measure variation, the proposed methods promise better surface reconstruction completeness and performance evaluation correctness. Simulations on the algorithm are performed on a typical parametric surface. A brief comparison to height-based scanning algorithm is performed to show the adaptability of the novel method on curvature evaluation. Experimental verifications are conducted to show the efficiency of the proposed algorithm.

Automated summary

This paper presents an adaptive scanning path generation method based on geometry information, which reduces data amount, increases scanning speed, and preserves sufficient geometric information for performance evaluation. The method generates grid points iteratively by obtaining surface geometry knowledge step by step, focusing on curvature properties. The proposed method promises better surface reconstruction completeness and performance evaluation correctness, with simulations and experimental verifications showing its efficiency.