GIXRF and machine learning as metrological tools for shape and element sensitive reconstructions of periodic nanostructures

Anna Andrle; Philipp Hönicke; Grzegorz Gwalt; Philipp-Immanuel Schneider; Yves Kayser; Frank Siewert; Victor Soltwisch

doi:10.1117/12.2586082

22 February 2021 GIXRF and machine learning as metrological tools for shape and element sensitive reconstructions of periodic nanostructures

Anna Andrle, Philipp Hönicke, Grzegorz Gwalt, Philipp-Immanuel Schneider, Yves Kayser, Frank Siewert, Victor Soltwisch

Author Affiliations +

Proceedings Volume 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV; 116110R (2021) https://doi.org/10.1117/12.2586082
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

The characterization of nanostructures and nanostructured surfaces with high sensitivity in the sub-nm range has gained enormously in importance for the development of the next generation of integrated electronic circuits. A reliable and non-destructive characterization of the material composition and dimensional parameters of nanostructures, including their uncertainties, is strongly required. Here, an optical technique based on grazing incidence X-ray fluorescence measurements is proposed. The reconstruction of a lamellar nanoscale grating made of Si3N4 is presented as an example. This technique uses the X-ray standing wave field, which arises due to interference between the incident and the reflected radiation, as nanoscale gauge. This enables the spatial distribution of the specific elements to be reconstructed using a finite-element method for the calculation of the standing wave field inside the material. For this, the optical constants for the constituent materials of the structure are needed. We derived them from soft X-ray reflectivity measurements on an unstructured part of the wafer sample. To counteract the expensive computation of the finite-element-Maxwell-solver, a Bayesian optimizer is exploited to obtain a most efficient sampling of the searched parameter space. The method is also used to determine the uncertainties of the reconstructed parameters. The homogeneity of the sample was also analyzed by evaluating several measurement spots across the grating area. For the validation of the reconstruction results, the grating line shape was measured by means of Atomic Force Microscopy.

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Anna Andrle, Philipp Hönicke, Grzegorz Gwalt, Philipp-Immanuel Schneider, Yves Kayser, Frank Siewert, and Victor Soltwisch "GIXRF and machine learning as metrological tools for shape and element sensitive reconstructions of periodic nanostructures", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 116110R (22 February 2021); https://doi.org/10.1117/12.2586082

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