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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
https://iopscience.iop.org/article/10.1088/1361-6501/ab8993