Extraction of Hidden Information of ToF-SIMS Data Using Different Multivariate Analysis

Abstract

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful tool for determining surface information of complex systems such as polymers and biological materials. However, the interpretation of ToF-SIMS rawdata is often difficult. Especially the identification of all spectra of complex samples including large molecules is difficult, since the ToF-SIMS spectrum data of polymer samples includes many fragment ion peaks originating in polymers and other contamination peaks. Multivariate analysis has become effective methods for the interpretation of ToF-SIMS data. Some of multivariate analysis methods such as principal component analysis and multivariate curve resolution are useful for simplifying ToF-SIMS data consisting of many components to that explained by a smaller number of components. In this study, the ToF-SIMS data of four layers of three polymers was analyzed using these analysis methods. The information acquired by using each method was compared in terms of the spatial distribution of the polymers and identification. Moreover, in order to investigate the influence of surface contamination, the ToF-SIMS data before and after Ar cluster ion beam sputtering was compared. As a result, materials in the sample of multiple components, including unknown contaminants, were distinguished.