Depth-Profiling by Confocal Raman Microscopy (CRM): Data Correction by Numerical Techniques

Abstract

The data obtained in confocal Raman microscopy (CRM) depth profiling experiments with dry optics are subjected to significant distortions, including an artificial compression of the depth scale, due to the combined influence of diffraction, refraction, and instrumental effects that operate on the measurement. This work explores the use of (1) regularized deconvolution and (2) the application of simple rescaling of the depth scale as methodologies to obtain an improved, more precise, confocal response. The deconvolution scheme is based on a simple predictive model for depth resolution and the use of regularization techniques to minimize the dramatic oscillations in the recovered response typical of problem inversion. That scheme is first evaluated using computer simulations on situations that reproduce smooth and sharp sample transitions between two materials and finally it is applied to correct genuine experimental data, obtained in this case from a sharp transition (planar interface) between two polymeric materials. It is shown that the methodology recovers very well most of the lost profile features in all the analyzed situations. The use of simple rescaling appears to be only useful for correcting smooth transitions, particularly those extended over distances larger than those spanned by the operative depth resolution, which limits the strategy to the study of profiles near the sample surface. However, through computer simulations, it is shown that the use of water immersion objectives may help to reduce optical distortions and to expand the application window of this simple methodology, which could be useful, for instance, to safely monitor Fickean sorption/desorption of penetrants in polymer films/coatings in a nearly noninvasive way.