Speaker
Description
For more than 40 years now, single wavelength and spectroscopic ellipsometry have been intensively used to determine optical properties as well as thickness of multilayered materials (see e.g. [1]). Principal components analysis (PCA) and hierarchical cluster analysis (HCA) are multivariate analysis methods respectively used for dimension data reduction and classification, respectively [2, 3]. They are now widely used to help in the processing of the surface analysis data, mainly XPS [4] and ToF-SIMS data [5]. On the contrary, they have only been applied to ellipsometry data in a (very) limited number of cases: characterization of gold nanocolloids [6] and identification of the optical response of microorganisms thin films [7].
In this contribution, we will briefly review PCA, HCA and support vector machines (SVM) to present new applications to ellipsometry data processing. Examples will be given from three original topics : (a) the characterization of the resonance plasmon parameters in nanocomposite thin films embedding silver nanoparticles, (b) the processing of imaging spectroscopic ellipsometry data cubes and (c) the synthesis of metal nanoparticles by laser annealing of polymer films.
Acknowledgments: This research was financially supported by the F.R.S.-FNRS (Grant 1926111).
[1] H. Tompkins and E. Irene, Handbook of Ellipsometry (William Andrew Pub., 2005).
[2] H. Grahn and P. Geladi, Techniques and applications of hyperspectral image analysis (J. Wiley, 2007).
[3] J. A. Hartigan, Clustering Algorithms (John Wiley & Sons, Inc., New York, NY, USA, 1975).
[4] P. Rouxhet and M. Genet, Surface and Interface Analysis 43, 1453 (2011).
[5] C. May, H. Canavan, and D. Castner, Analytical Chemistry 76, 1114 (2004).
[6] E. M. Brouwer, E. S. Kooij, H. Wormeester, M. A. Hempenius, and B. Poelsema, Journal of Physical Chemistry B: Materials, surfaces, interfaces, & biophysical 108, 7748 (2004).
[7] E. Garcia-Caurel, J. Nguyen, L. Schwartz, and B. Drévillon, Thin Solid Films 455-456, 722 (2004).
