Methods for topography artifacts compensation in scanning thermal microscopy

Abstract

Thermal conductivity contrast images in scanning thermal microscopy (SThM) are often distorted by artifacts related to local sample topography. Three methods for numerically estimating and compensating for topographic artifacts are compared in this paper: a simple approach based on local sample geometry at the probe apex vicinity, a neural network analysis and 3D finite element modeling of the probe–sample interaction. A local topography and an estimated probe shape are used as source data for the calculation in all these techniques; the result is a map of false conductivity contrast signals generated only by sample topography. This map can be then used to remove the topography artifacts from measured data.Thermal conductivity contrast images in scanning thermal microscopy (SThM) are often distorted by artifacts related to local sample topography. Three methods for numerically estimating and compensating for topographic artifacts are compared in this paper: a simple approach based on local sample geometry at the probe apex vicinity, a neural network analysis and 3D finite element modeling of the probe–sample interaction. A local topography and an estimated probe shape are used as source data for the calculation in all these techniques; the result is a map of false conductivity contrast signals generated only by sample topography. This map can be then used to remove the topography artifacts from measured data.