XPS spectromicroscopy: exploiting the relationship between images and spectra

The application of spectroscopic processing techniques to multi‐spectral XPS data sets has enabled the acquisition of quantitative surface chemical state images. Such data sets are necessarily large, incorporating many spectra, so prohibiting interactive processing. Instead multivariate analytical techniques are used to reduce the dimensionality of the data, and also to increase the signal/noise, there bye speeding acquisition. These techniques may also be used to classify regions in images according to different chemistry, that is changes in photoelectron intensity, changes in binding energy and changes in the inelastic background. Spectra from classified regions may then be summed to aid visualisation, obviating the need for multivariate curve resolution with its attendant uncertainties. Further the inelastic background of transmission corrected spectra from classified regions may be modelled to provide spatially resolved in‐depth information. Such classification also aids curve fitting, since curve fit models can be applied to regions of similar chemistry.