We have developed a procedure for fitting experimental and simulated X-ray reflectivity and diffraction data in order to automate and to quantify the characterization of thin-film structures. The optimization method employed is a type of genetic algorithm called 'Differential Evolution'. The method is capable of rapid convergence to the global minimum of an error function in parameter space even when there are many local minima in addition to the global minimum. We show how to estimate the pointwise errors of the optimized parameters, and how to determine whether the model adequately represents the structure. The procedure is capable of fitting some tens of adjustable parameters, given suitable data.

With the continued scaling of CMOS devices below the 10 nm node, process technologies become more and more challenging as the allowable thermal budget for device processing continuously reduces. This is especially the case during epitaxial growth, where a reduction of the thermal budget is required for a number of potential reasons for example to avoid uncontrolled layer relaxation of strained layers, surface reflow of narrow fin structures, as well as doping diffusion and material intermixing. Further aspects become even more challenging when Ge is used as a high-mobility channel material and when the device concept moves from a FinFET design to a nanowire FET design (also called Gate-All-Around FET). In this contribution we address some of the challenges involved with the integration of high mobility Group IV materials in these advanced device structures.

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