Search

Determination of the depth distribution of complex nanostructures by X-ray photoelectron spectroscopy (XPS) inelastic background analysis may be complicated if the sample materials have widely different inelastic scattering cross-sections. It was recently demonstrated that this may be solved by using a mixture of cross-sections. This permits retrieval of depth distributions of complex stacks and deeply buried layers with a typical 5% accuracy. This requires however that the cross-sections of the individual sample materials are known which is often not the case and this can complicate practical use for routine analysis. In this paper, we explore to what extent a suitable two- or three-parameter cross-section can be defined independent of prior knowledge of the cross-sections involved but simply defined by fitting the cross-section parameters to the spectrum being analyzed. This paper presents a theoretical study following our recent paper that explored how to make the best choice of inelastic mean free path and inelastic scattering cross-section for the inelastic background analysis with the Quases-Tougaard software. It was previously shown that a rough analysis of the inelastic background could give a good idea of the depth distribution. Here, we demonstrate with model spectra from buried layers created with Quases-Tougaard Generate software that a rather accurate analysis can be performed for very different cases with an average ~5% error. This analysis is easy to apply as it only needs the two- or three-parameter cross-sections generated with the Quases-Tougaard software. This study is aimed to improve routine analysis of the inelastic background of XPS and hard X-ray photoelectron spectroscopy (HAXPES) spectra.

Gallium nitride (GaN) grown on Si by metalorganic chemical vapor deposition was analyzed using high-resolution, high-energy x-ray photoelectron spectroscopy (HAXPES). The HAXPES spectra of GaN obtained using monochromatic Cr Kα radiation at 5414.7 eV include a survey scan (Al Kα) and high-resolution spectra of Ga 3d, Ga 2p3/2, Ga 3p, Ga LMM, N 1s, C 1s, and O 1s.

X-ray Photoelectron Spectroscopy (XPS) is a widely used surface analysis technique with many well established industrial and research applications. The surface sensitivity (top 5-10 nm) of XPS and its ability to provide short-range chemical bonding information make the technique extremely popular in materials characterization and failure analysis laboratories. While its surface sensitivity is an important attribute, in some cases, the depth of analysis of XPS is not sufficient to analyze buried interfaces without first sputter etching the sample surface. However, sputter etching can often lead to alterations of the true surface chemistry. An alternative to sputter etching the sample is Hard X-ray Photoelectron Spectroscopy (HAXPES), available at some synchrotron facilities. HAXPES utilizes X-rays typically defined as having energies greater than 5 keV. By increasing the photon energy of the X-ray source, the mean free path of photoelectrons is increased, resulting in an increased information depth obtained from the sample. Depending on the energy used, these hard X-rays can provide depths of analysis three or more times than that of soft x-rays used on conventional XPS systems. HAXPES measurements are, therefore, more sensitive to the bulk, and contributions from the surface are minimized.1,2 This presentation will describe a laboratory-based instrument, the PHI Quantes, equipped with two scanning microprobe monochromated X-ray sources, Al Kα (1486.6 eV) and Cr Kα (5414.9 eV), thus enabling both traditional XPS and HAXPES experiments in the same instrument. Combining both soft and hard X-ray analyses, we can gain an even better understanding of composition with depth and information at buried interfaces. References Kobayashi, K. Hard X-ray photoemission spectroscopy, Nucl. Instr. Meth. Phys. Res. A 2009, 601, 32-47. Fadley, C.S. Hard X-ray Photoemission: An Overview and Future Perspective. In Hard X-ray Photoelectron Spectroscopy (HAXPS); Woicik, J. C., Ed; Springer: Switzerland 2016.