How levelling and scan line corrections ruin roughness measurement and how to prevent it

Abstract

Surface roughness plays an important role in various fields of nanoscience and nanotechnology. However, the present practices in roughness measurements, typically based on some Atomic Force Microscopy measurements for nanometric roughness or optical or mechanical profilometry for larger scale roughness significantly bias the results. Such biased values are present in nearly all the papers dealing with surface parameters, in the areas of nanotechnology, thin films or material science. Surface roughness, most typically root mean square value of irregularities Sq is often used parameter that is used to control the technologies or to link the surface properties with other material functionality. The error in estimated values depends on the ratio between scan size and roughness correlation length and on the way how the data are processed and can easily be larger than 10% without us noting anything suspicious. Here we present a survey of how large is the problem, detailed analysis of its nature and suggest methods to predict the error in roughness measurements and possibly to correct them. We also present a guidance for choosing suitable scan area during the measurement.Surface roughness plays an important role in various fields of nanoscience and nanotechnology. However, the present practices in roughness measurements, typically based on some Atomic Force Microscopy measurements for nanometric roughness or optical or mechanical profilometry for larger scale roughness significantly bias the results. Such biased values are present in nearly all the papers dealing with surface parameters, in the areas of nanotechnology, thin films or material science. Surface roughness, most typically root mean square value of irregularities Sq is often used parameter that is used to control the technologies or to link the surface properties with other material functionality. The error in estimated values depends on the ratio between scan size and roughness correlation length and on the way how the data are processed and can easily be larger than 10% without us noting anything suspicious. Here we present a survey of how large is the problem, detailed analysis of its nature and suggest methods to predict the error in roughness measurements and possibly to correct them. We also present a guidance for choosing suitable scan area during the measurement.