Grazing incidence-X-ray fluorescence for a dimensional and elemental characterization of well-ordered nanostructures

Abstract

The increasing importance of well-controlled ordered nanostructures on surfaces represents a challenge for existing metrology techniques. To develop such nanostructures and monitor complex processing constraints fabrication, both a dimensional reconstruction of nanostructures and a characterization (ideally a quantitative characterization) of their composition is required. In this work, we present a soft X-ray fluorescence-based methodology that allows both of these requirements to be addressed at the same time. By applying the grazing-incidence X-ray fluorescence technique and thus utilizing the X-ray standing wave field effect, nanostructures can be investigated with a high sensitivity with respect to their dimensional and compositional characteristics. By varying the incident angles of the exciting radiation, element-sensitive fluorescence radiation is emitted from different regions inside the nanoobjects. By applying an adequate modeling scheme, these datasets can be used to determine the nanostructure characteristics. We demonstrate these capabilities by performing an element-sensitive reconstruction of a lamellar grating made of Si${}_3$N${}_4$, where GIXRF data for the O-K$\alpha$ and N-K$\alpha$ fluorescence emission allows a thin oxide layer to be reconstructed on the surface of the grating structure. In addition, we employ the technique also to three dimensional nanostructures and derive both dimensional and compositional parameters in a quantitative manner.

arXiv comment7 pages, 7 figures

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Cited by in corpus (4)

• Shape- and element-sensitive reconstruction of periodic nanostructures with grazing incidence X-ray fluorescence analysis and machine learning
• Experimental and theoretical approaches for determining the K-shell fluorescence yield of carbon
• Hybrid approach to reconstruct nanoscale grating dimensions using scattering and fluorescence with soft X-rays
• Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction