An approach is presented to apply fractal concepts (by analyzing scanning length-dependent scanning tunneling microscopy measurements) to the characterization of semiconductor surfaces exposed to various, technologically relevant, cleaning processes. In particular, we have investigated the surface morphology and chemical composition of Ge(100) surfaces after cleaning in an aqueous HF solution and vacuum annealing. The roughness exponent alpha, a characteristic parameter to describe self-affine fractal surfaces, depends on the cleaning procedure and correlates to the surface chemical composition. For temperatures close to 500 degrees C, atomic diffusion and desorption processes reduce the roughness exponent, which is equivalent to an increase in the sub-micron scale roughness. At temperatures above 500 degrees C, surface smoothening mechanisms are initiated, leading to increasing roughness exponent values. In strong contrast, no pronounced changes in the surface roughness were observed on a large scale (saturation roughness), i.e. beyond the regime of self-affine scaling. Our findings highlight the significance of using scanning length-dependent roughness analysis to quantitatively assess variations in surface morphology after different cleaning processes and to directly compare various complex surfaces. (C) 2011 The Electrochemical Society. [DOI: 10.1149/1.3624762] All rights reserved.