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NEČAS, D. KLAPETEK, P. VALTR, M.
Originální název
Estimation of roughness measurement bias originating from background subtraction
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
scanning probe microscopy; data processing; roughness; bias; levelling; autocorrelation
Autoři
NEČAS, D.; KLAPETEK, P.; VALTR, M.
Vydáno
1. 9. 2020
Nakladatel
IOP PUBLISHING LTD
Místo
BRISTOL
ISSN
0957-0233
Periodikum
Measurement Science and Technology
Ročník
31
Číslo
9
Stát
Spojené království Velké Británie a Severního Irska
Strany od
094010-1
Strany do
094010-15
Strany počet
15
URL
https://iopscience.iop.org/article/10.1088/1361-6501/ab8993