Terblans, J. J.Swart, H. C.Joubert, Heinrich Daniel2015-11-242015-11-24201020102010http://hdl.handle.net/11660/1799Thin film diffusion studies often involve a surface sensitive analysis technique combined with ion erosion to produce a depth profile of a sample. Such studies compare the depth profile of a reference sample to the depth profiles of samples that were annealed at different temperatures and times. The extent to which atoms of one layer diffuse into an adjacent layer, for a particular temperature and time, yields information on the diffusion process involved and allows quantification of the diffusion coefficient. The drawback to using an erosion type system is the effect of the incident ions on the surface being probed. The Mixing-Roughness-Information model attempts to compensate for this effect and is often employed as a means of quantification of measured depth profiles by means of profile reconstruction. Used in conjunction with Auger electron spectroscopy, the Mixing-Roughness-Information (MRI) model is a useful tool to reconstruct the ion erosion depth profiles as well as extracting inter-diffusion parameters from these depth profiles. The first part of the study focuses on the extraction of the diffusion coefficient of classically annealed samples of Ni in Cu from Ni/Cu depth profiles obtained from ion erosion Auger electron spectroscopy. The resultant depth profiles were reconstructed with the MRI model. The diffusion coefficient for Ni diffusing in Cu was obtained from the MRI fit and it compared well to values available in literature. From an Arrhenius graph a value of 9 2 -1 D0 6.49 10-9 m2 .s-1 for the pre-exponential factor and Q =130.5 kJ.mol-1 for the activation energy was calculated. The second part of the study involves linear ramping as an annealing technique. In previous studies, linear temperature ramping was used to determine diffusion coefficients from bulk-to-surface segregation experiments of a low concentration solute. Thin film diffusion studies usually employ a classical heating regime, where a sample’s annealing time is taken as the time between insertion and removal from a furnace. The aforementioned study type assumes that the time it takes to heat a sample after insertion is instantaneous, while the sample cools down instantaneously after removal from the furnace. This assumption is incorrect, as it does not compensate for the various mechanisms that govern heat transfer. In order to eliminate the uncertainty, a linear ramping regime is used and samples were annealed inside an UHV environment with a programmed linear heating scheme. After each anneal, a depth profile was obtained by simultaneously bombarding the sample with Ar+ ions and monitoring the exposed surface with an electron beam which excites Auger electrons, among others. The depth profiles were normalised and the time scale converted to depth. In order to compare the diffusion profiles obtained from classical annealing studies to the linearly ramped studies, the diffusion coefficient obtained for a classical study of Ni diffusing in Cu was compared to the diffusion coefficient obtained from a MRI linear ramp analysis of the ramped samples. The linear ramp analysis yielded a pre-exponential factor of 13 2 -1 D0 2.29 10-13 m2 .s-1 and activation energy of Q= 82.5 kJ.mol-1. Comparison of the diffusion profiles calculated with the diffusion coefficients obtained from classical heating and linear heating showed a large discrepancy between the calculated diffusion profiles. Analysis of the calculated profiles showed that classical diffusion studies overestimate the rate of diffusion if compared to the diffusion profile calculated with diffusion parameters obtained from linear ramping experiments. The linear ramping MRI technique was extended even further by changing the heating and cooling rate, thereby decreasing the effective annealing time. Diffusion profiles obtained from the extended linear heating MRI method refined the diffusion parameters for linear ramping even further.enThesis (Ph.D. (Physics)--University of the Free State, 2010DiffusionThin filmsAuger electron spectroscopyNiCuDepth resolution functionMixing-Roughness-Information (MRI) modelMatrix factorsLinear temperature rampingShimizuLinear Least Squares (LLS)Activation energyDepth profilingThesisUniversity of the Free State