Deep learning-enabled vertical drift artefact correction for AFM images

Abstract

3D topography imaging systems such as Atomic Force Microscopy (AFM) are used for surface characterization and metrology in numerous contexts especially when nanometer resolution is required (e.g. semiconductor industry & research). During the acquisition of an AFM image often a drift is present in vertical direction that is superimposed on top of the topography signal. This represents an artefact that cannot be removed with a single one-size-fits-all algorithm and typically requires manual input and expert assessment whether the correction is done appropriately. Hence, the final result is operator dependent. In this work we propose a method to correct various artifacts that arise from vertical (Z) drift that can be regarded a superimposed envelope (ENV) on top of the true topography of the sample. We remove this envelope with the help of processing the raw image data with the help of Deep Neural Networks. Moreover, we employ a normalization scheme for pixel intensities for the preservation of absolute vertical height values for corrected images thus allowing for quantitative measurements of topography for metrology needs. Our approach allows for automatic and operator independent data correction, leading to more robust data analysis and interpretation, enabling faster speed of learning