201517th International Congress of Metrology
|Number of page(s)
|Nanotechnologie : mesure et caractérisation / Nanotechnology: measurement and characterisation
|21 September 2015
Model-aided hybrid metrology for surface roughness measurement fusing AFM and SEM data
Institute of Nanoscience and Nanotechnology, NCSR Demokritos, Aghia Paraskevi, Greece
In this paper, we propose a hybrid metrology approach to the measurement and characterization of the nanoroughness of freeform film surfaces. The basic idea is to combine measurement data from Atomic Force and Scanning Electron Microscopes (AFM and SEM respectively) in a synergetic manner exploiting the advantages of both methods and reducing the effects of their shortcomings. The fusion of data is realized through a model reconstruction of the measured rough surface. In particular, the hybrid approach is implemented by obtaining the height distribution function from AFM topographies (given the high accuracy in AFM height measurements) and the autocorrelation function or Fourier transform of the surface morphology from SEM images (given the high spatial resolution in SEM images). These functions are then used as input to an algorithm to generate rough surfaces in which AFM tip size effects are minimized and hence are more accurate statistical representation of the real surface. The output morphologies may be employed to estimate all roughness parameters and metrics and especially the so-called hybrid parameters (active surface area, distributions of surface derivatives and curvatures etc.) which depend on both vertical and spatial roughness aspects. The latter parameters may be critical in many applications where surface roughness is used to control wetting behaviour, light scattering, bioadhesion or wear properties. As an example, we apply the hybrid approach to the estimation of the active surface area of a sample of cyclic olefin film etched in oxygen plasma for wetting control.
© Owned by the authors, published by EDP Sciences, 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.