The Monte Carlo (MC) approach is a universal tool for describing electron transport in condensed matter and for developing descriptive formulas applicable to quantitative applications of XPS and other electron spectroscopies. Any MC simulation strategy requires implementation of samplers that generate certain parameters that are needed for determining an electron trajectory in the surface region of a sample material. Two parameters are of considerable importance in MC strategies: (i) the cross sections for elastic scattering of electrons by atoms in the surface region, and (ii) the inelastic mean free path (IMFP) of electrons in the sample material.

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We have developed a framework that integrates the XPS simulator SESSA into Bayesian estimation to solve the inverse problem of XPS. This approach automates the typically labor-intensive task of manually adjusting sample structure parameters for XPS simulation. By incorporating Bayesian methods, we can estimate the distribution of plausible sample structures based on experimental XPS data, providing not only an optimal solution but also a detailed visualization of the solution's distribution. In a case study, we performed angle-resolved XPS on a four-layered sample and successfully estimated the sample structure using this framework. This method streamlines the analysis of XPS data and offers a comprehensive view of sample structures, marking a significant step forward in the application of data science techniques to experimental data.

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Quantitative surface analysis of the chemical state of copper materials is important for understanding the bonding process between copper and different materials. X-ray photoelectron spectroscopy (XPS) is expected to be able to distinguish between 0- to 2-valent copper compounds based on the positions of photoelectron and Auger electron peaks. However, quantitative component analysis of multiphase copper compounds using XPS has been difficult because the difference in chemical shifts between copper with different valences is small and the fine structure of the XPS spectrum varies depending on the copper compound type even for the same valence. In this study, we applied a recently developed technique to simultaneously analyze both Auger and photoelectron peaks automatically and comprehensively in the case of the depth profiling of naturally oxidized copper, and to establish a quantitative evaluation method for the chemical state of copper compounds.

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X線光電子分光法（XPS: X-ray Photoelectron Spectroscopy）において，一般に用いられるAl Kα線（エネルギー 1486.6 eV）で検出できる光電子の情報深さは5～10 nm程度である．一方，より高エネルギーのX線を使用した硬X線光電子分光法（HAXPES: Hard X-ray Photoelectron Spectroscopy）では，Cr Kα線（5414.8 eV）を利用することで，情報深さをAl Kα線の約3倍まで拡張することが可能である．Cr Kα線のより深い情報深さを利用することで，スパッタリングによるダメージを回避することができ，さらに，HAXPESと角度分解測定の組み合わせにより，より深部の濃度分布情報を取得することができる．本稿では，これらの測定の実試料による応用事例を紹介する．

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