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
The ever-increasing complexity of materials and architectures in nanoelectronics devices has driven the demand for new high-resolution imaging methods. Specifically, for three-dimensional (3D) analysis of confined volumes, atomic force microscopy (AFM) has been recently explored as a method for tomographic sensing. Here, we report on the innovative design of a dedicated microscopy solution for volumetric nanoscale analyses that achieves tomographic AFM by using a novel multi-probe sensing architecture. First, we describe the development of a custom scan head that is based on an exchangeable multi-probe hardware. Second, we demonstrate the use of our machine for tip-induced material removal in thick SiO2. Finally, we perform a tomographic reconstruction of nanosized poly-Si vertical channels, considered here as a prototypical system for vertical memory cells.
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