Optical Properties of Germanium in the Far Ultraviolet

Marton, L.; Toots, J.

doi:10.1103/physrev.160.602

Cited by 58 publications

(13 citation statements)

References 22 publications

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“…As a result, BFs may depend on the emission angle although it has been found that this dependence is not very pronounced. 42 We expect that BFs obtained from the MC schemes A and B considered here would also be similar if the calculations were based on the more accurate definition of the BF instead of Eqn (26).…”

Section: Discussionmentioning

confidence: 87%

“…27 Let us tentatively Figure 2. Percentage differences, S, between stopping powers from optical data and from other sources calculated using Eqn (26). Solid line: Bethe expression (Eqn (3)); long-dashed line: modification of the Bethe expression by Rao-Sahib and Wittry (Eqn (9)); short-dashed line: stopping power derived from the two-parameter universal inelastic-scattering cross section (Eqns (16) and (17)); dotted line: stopping power derived from the three-parameter universal inelastic-scattering cross section (Eqns (17) and (19)- (22) accept values of S opt E as reference values, and determine the percentage deviations from S opt E of the stopping-power functions considered here:…”

Section: Comparison Of the Energy Dependences Of The Stopping Powermentioning

confidence: 99%

“…We performed calculations of the BF from Eqn (26). The inner-shell ionization cross sections were calculated from the Casnati et al 28 formula.…”

Section: Monte Carlo Schemes For Simulations Of Electron Transport Inmentioning

confidence: 99%

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Monte Carlo strategies for simulations of electron backscattering from surfaces

Jabłoński

Powell

Tanuma

2005

Surface & Interface Analysis

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Reliable algorithms for simulation of electron trajectories in solids are needed for quantification of Auger-electron spectroscopy, particularly for the determination of the backscattering factor (BF). The computational schemes for this purpose should be universal, i.e. applicable to any solid for energies down to 50 eV. In addition, the algorithms should ideally require simple input parameters, e.g. the stoichiometry of the solid and the experimental configuration. Previous Monte Carlo simulations have often been based on the continuous slowing down approximation (CSDA), a relatively simple and convenient approach. However, this approach requires information on the electron stopping power for a given solid. General expressions for the stopping power are briefly reviewed and discussed. We report results of BF calculations for Cu M 3 VV, Ag M 5 VV, and Au N 67 VV Auger transitions from Monte Carlo simulations based on the CSDA in which stopping powers from optical data and from Tougaard's two-parameter universal inelastic-scattering cross section were employed. The resulting BFs, for primary energies between 500 eV and 10 keV, differed by up to 12% (at an energy of 10 keV). BFs from the Monte Carlo algorithm based on the CSDA were compared with similar results from a more sophisticated code involving simulations of individual elastic-and inelastic-scattering events. Both algorithms yielded equivalent BFs for the same Auger transitions with primary energies exceeding 1000 eV; at lower energies, deviations up to 10% occurred because of an approximation in the evaluation of an integral.

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Section: Discussionmentioning

confidence: 87%

Section: Comparison Of the Energy Dependences Of The Stopping Powermentioning

confidence: 99%

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Monte Carlo strategies for simulations of electron backscattering from surfaces

Jabłoński

Powell

Tanuma

2005

Surface & Interface Analysis

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“…To analyze the differences between the simple Ritchie-Howie, Ritchie-Howie, FPA, and SPA methods in modeling the dielectric function, we performed calculations of the momentum transfer-dependent ELFs of Ge based on these approximations. The optical data of Ge were taken from the measured data [63,67,68]. For the simple Ritchie-Howie model and the Ritchie-Howie model, we need 3N parameters of Drude-type oscillators rather than the specific values of optical ELF as the inputs for the calculation.…”

Section: A Comparing Models Describing the Dielectric Functionmentioning

confidence: 99%

“…However, the positions and intensities of the three peaks are different for the present result and Palik's data. [44,63,67,68] for Ge.…”

Section: Simentioning

confidence: 99%

Optical properties of silicon and germanium determined by high-precision analysis of reflection electron energy loss spectroscopy spectra

et al. 2019

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We present a detailed analysis and comparison of four models describing the extension of the electron-energy loss function from the optical limit of q→0 into the (q,ω) plane to obtain the bulk and surface terms of differential inverse inelastic mean free paths. We found that the best model that describes accurately and times efficiently the calculation of the energy loss function of free-electron-like materials is the combination of the Penn algorithm [Phys. Rev. B 35, 482 (1987)] with the Ritchie-Howie method [Philos. Mag. 36, 463 (1977)]. Applying this model in our reverse Monte Carlo method, we determined, with high-precision, electron-energy loss functions of silicon and germanium based on the theoretical analysis of the high-energy resolution reflected electron energy loss spectroscopy (REELS) spectra, measured at 3, 4, and 5 keV incident electron energies. The refractive index n, the extinction coefficient k, and the complex dielectric function (ε = ε 1 + iε 2 ) were calculated from the obtained energy loss function in a wide energy loss range of 0-200 eV. The accuracy of the obtained results is justified with various sum rules. We found that the calculated optical data of Si and Ge fulfill the sum rules with an average accuracy of 0.11% or even better. Therefore, the use of these optical data in materials science and surface analysis is highly recommended for further applications.

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