Classification of secondary ion mass spectrometry (SIMS) micrographs to characterize chemical phases

Abstract

Abstract. This work demonstrates the potential of multivariate image analysis methods in the extraction of useful, problem dependent information from SIMS images. Specific algorithms have been developed to classify SIMS micrographs manually as well as automatically. A feature selection has been achieved by means of principal component analysis with a subsequent image classification.As an application example for these improved digital image processing tools chemical phases within a soldered industrial metal sample have been identified. This is of highly practical value as it was assumed that during the soldering process inhomogeneities occur along the joint site which cause a cracking of the brazed material under mechanical strain conditions. Key words: secondary ion mass spectrometry (SIMS), imaging, classification, principal component analysis (PCA).Digital image analysis, initially developed in the sixties for exploration purposes, has spread as a new powerful tool over a whole variety of disciplines. Physical dimensions do not set any limits to its application possibilities: ranging from satellite based sensing in space, down to the atomic level of scanning tunneling microscopy [1], digital image processing techniques are now extensively employed. They have also proved to be very attractive for chemical analytical aims where they provide the advantage of visualization of data otherwise difficult to grasp, for example because of very large data sets, and also the advantage of possible quantification of purely optical, qualitative information. The digitized images used for such operations are statistical to the core because a numerical value can be allocated to each to their pixels and therefore they obviously contain more information than mere size or shape. Digital image processing techniques applied to such micrographs directly induce a calculation of the underlying data. Vice versa, statistical operations within

Evaluation of mass spectra from organic compounds assumed to be present in cometary grains. Exploratory data analysis

“…Recently, laser desorption mass spectral data from airborne particles were interpreted by fuzzy clustering and other multivariate data analysis methods [10±12]. Classification of areas in SIMS images from surface analysis allowed the automatic detection of inorganic phases [13,14]. Cluster analysis has been applied for a variable alignment of high-resolution TOF-SIMS data from steel samples [15].…”

Chemometric evaluation of time‐of‐flight secondary ion mass spectrometry data of minerals in the frame of future in situ analyses of cometary material by COSIMA onboard ROSETTA

“…Application of these data evaluation methods could be directly relevant to the data that will be returned by COSIMA. Chemometrics has been only rarely applied to TOF‐SIMS data, for instance, in studies of inorganic material,19 and aerosol particles,20, 21 as well as for image analysis of soldering inhomogeneities 22. However, chemometrics has been extensively used with electron impact (EI) mass spectra of organic compounds,23, 24 even relevant to comet chemistry 25, 26.…”

Classification of SIMS Images Using a Kohonen Network

“…In scanning mode, energy filtering is possible (with some instruments) to overcome this problem; in imaging mode, this would lead to a significant deterioration of the image quality. Numerous chemometric approaches to this problem, like the deconvolution of low-resolution mass spectra 14 or the minimum distance classificator, , have been reported. This paper describes the use of Kohonen networks for assigning structures in secondary ion distributions to chemical phases.…”