Publication detail

Estimation of roughness measurement bias originating from background subtraction

NEČAS, D. KLAPETEK, P. VALTR, M.

Original Title

Estimation of roughness measurement bias originating from background subtraction

Type

journal article in Web of Science

Language

English

Original Abstract

When measuring the roughness of rough surfaces, the limited sizes of scanned areas lead to its systematic underestimation. Levelling by polynomials and other filtering used in real-world processing of atomic force microscopy data increases this bias considerably. Here a framework is developed providing explicit expressions for the bias of squared mean square roughness in the case of levelling by fitting a model background function using linear least squares. The framework is then applied to polynomial levelling, for both one-dimensional and two-dimensional data processing and basic models of surface autocorrelation function, Gaussian and exponential. Several other common scenarios are covered as well, including median levelling, intermediate Gaussian-exponential autocorrelation model and frequency space filtering. Application of the results to other quantities, such as Rq, Sq, Ra and Sa is discussed. The results are summarized in overview plots covering a range of autocorrelation functions and polynomial degrees, which allow graphical estimation of the bias.

Keywords

scanning probe microscopy; data processing; roughness; bias; levelling; autocorrelation

Authors

NEČAS, D.; KLAPETEK, P.; VALTR, M.

Released

1. 9. 2020

Publisher

IOP PUBLISHING LTD

Location

BRISTOL

ISBN

0957-0233

Periodical

Measurement Science and Technology

Year of study

31

Number

9

State

United Kingdom of Great Britain and Northern Ireland

Pages from

094010-1

Pages to

094010-15

Pages count

15

URL