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We investigated silicon nanoclusters Si(nc) in a SiO 2 matrix prepared by the plasma-enhanced chemical vapor deposition technique, using X-ray photoelectron spectroscopy (XPS) with external voltage stimuli in both static and pulsed modes. This method enables us to induce an additional charging shift of 0.8 eV between the Si2p peaks of the oxide and the underlying silicon, both in static and time-resolved modes, for a silicon sample containing a 6 nm oxide layer. In the case of the sample containing silicon nanoclusters, both Si2p peaks of Si(nc) and host SiO 2 undergo a charging shift that is 1 order of magnitude larger (>15 eV), with no measurable difference between them (i.e., no differential charging between the silicon nanoclusters and the oxide matrix could be detected). By use of a measured Auger parameter, we estimate the relaxation energy of the Si(nc) in the SiO 2 matrix as -0.4 eV, which yields a -0.6 eV shift in the binding energy of the Si(nc) with respect to that of bulk Si in the opposite direction of the expected quantum size effect. This must be related to the residual differential charging between the silicon nanoclusters and the oxide host. Therefore, differential charging is still the biggest obstacle for extracting size-dependent binding energy shifts with XPS when one uses the oxide peak as the reference.

» Single-layer films and multiple-layer films can be quan titatively analyzed using X-ray fluorescence. Due to the absence of adequate standards, most methods are based on the calculation of theoretical X-ray fluorescence inten sities from fundamental parameter methods. These meth ods require as initial estimates the exact qualitative sample structure and accurate starting values for both concentrations and layer thicknesses. This paper proposes a fundamental parameter method that uses a genetic algorithm as an optimization procedure. The relaxation of the requirements on the description of the sample due to this robust optimization is discussed. Preliminary results are presented indicating possible applications as well as areas for further research.

A two-step fundamental parameter method for model-free analysis of thin-layered materials by X-ray fluorescence spectrometry is presented. In the first step, a genetic algorithm is used to obtain the number of layers and, for each layer, an estimate of the elementary concentrations and thickness. The second step is a gradient technique to refine this estimate. Good results are obtained for both relatively simple and more complex samples. The latter require extra depth information, which can be obtained from X-ray fluorescence measurements at various angles of detection.In today's technology, an increasing use is made of materials consisting of thin layers with thicknesses that can vary from only several nanometers to a few micrometers. They are applied for their optical, mechanical, electrical, and/or magnetic properties. An important application is the use of multiple thin layers of metal on silicon wafers in the integrated circuit technology. These materials can be quantitatively analyzed by X-ray fluorescence, where both layer thicknesses and elementary concentrations can be determined simultaneously.Since adequate standards for layered materials scarcely exist, a so-called fundamental parameter method is most often used. Fundamental parameter methods are based on the calculation of theoretical X-ray intensities. First, the sample is described in terms of layers with estimated concentrations and thicknesses. This estimated model is iteratively adapted until measured and predicted intensities are consistent, according to an error criterion. The final concentrations and thicknesses obtained are assumed to represent the actual composition.Currently applied fundamental parameter methods require an estimated sample model in which the number of layers and the distribution of the elements over the layers are fixed. The complexity of the sample and the optimization algorithm determine the required accuracy for the corresponding elementary concentrations and layer thicknesses.This paper explores to what extent less adequate initial estimates can lead to good quantitative results. The method used is an adapted version of the method described in ref 1. This new method consists of two optimization steps. In the first step, a genetic algorithm is used to obtain the number of layers, for each layer the elements, and an estimate of the corresponding concentrations and layer thickness. The second step is a gradient technique to refine this extracted model. Good results are obtained for both relatively simple and more complex samples. The latter require X-ray fluorescence data measured at various angles of detection. This is discussed below in some detail. METHODSThe complete description of a layered material consists of a qualitative model and corresponding quantitative parameters. The qualitative model is defined by the number of layers M and, for each individual layer m, the number of elements N m and their identities. The quantitative parameters are the corresponding concentrations and layer thicknesses. In the remainder o...

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