| AVS 54th International Symposium | |
| Applied Surface Science | Monday Sessions |
| Session AS-MoA |
| Session: | Quantitative Surface Analysis II. Electron Spectroscopies: (Honoring the contributions of Martin Seah, NPL, and Cedric Powell, NIST) |
| Presenter: | I.S. Gilmore, National Physical Laboratory, UK |
| Authors: | J.L.S. Lee, National Physical Laboratory, UK I.S. Gilmore, National Physical Laboratory, UK M.P. Seah, National Physical Laboratory, UK |
| Correspondent: | Click to Email |
Surface topography is a crucial issue for the analysis of innovative devices such as microfluidic systems, MEMS devices, fibres, composite materials, sensors, organic electronics and biomedical devices. The strength and durability of these components is critically dependent on their nanoscale surface chemistry and molecular interactions. However, quantitative characterisation of surfaces with topography remains a significant challenge due to the lack of systematic and validated measurement methods.1 In particular, surface topography can causes many unwanted artefacts in ToF-SIMS spectra and images, including ion shadowing effects caused by distortions in the extraction field, reduced mass resolution caused by the spread in the time of flight of secondary ions, distortions in the images due to the angular differences between the primary ion beam and the analyser with respect to the sample, loss in signal due to the limited angular acceptance of the analyser, and potential problems in charge compensation of insulating samples. This presents enormous technical challenges to process engineers and R&D scientists developing new products and processes. Here, we present a systematic study of the effects of surface topography on SIMS. Experimental data are acquired for model cylinders, fibres and spheres, for both conducting and insulating samples. The results are in good agreement with those obtained using an ion optics simulation program, SIMION,2 allowing us to understand the effects of surface topography and provide guidance to practical analysts for identifying and reducing topographical effects. In addition, the use of multivariate methods for images with surface topography is investigated using principal component analysis (PCA) and multivariate curve resolution (MCR), extending from our previous work on mixed organic systems on flat surfaces.3 With careful application and suitable data preprocessing methods, multivariate analysis is shown to improve data interpretation and allows for the rapid processing of high-resolution raw spectral data in SIMS images.
1 S Rangarajan and B J Tyler, J. Vac. Sci. Technol. A 24(5) (2006) 1730-1736
2 SIMION version 8.0, Scientific Instrument Services, Inc., 1027 Old York Rd., Ringoes, NJ 08551, USA
3 J L S Lee, I S Gilmore and M P Seah, submitted.