This paper shows how X-ray computed nanotomography (CNT) can be correlated with focused ion beam time-of-flight secondary ion mass spectrometry (FIB-TOF-SIMS) tomography on the same sample to investigate both the morphological and elemental structure. This methodology is applicable to relatively large specimens with dimensions of several tens of microns whilst maintaining a high spatial resolution of the order of 100 nm. However, combining X-ray CNT and FIB-TOF-SIMS tomography requires innovative sample preparation protocols to allow both experiments to be conducted on exactly the same sample without chemically or structurally modifying the sample between measurements. Moreover, dedicated algorithms have been developed for effective data fusion that is biased with nine degrees of freedom. This methodology has been tested using a porous and heterogeneous solid oxide fuel cell (SOFC) that has features varying in size by three orders of magnitude - from hundreds of nanometre large pores and grains to tens of micron wide functional layers.

In this paper the potential of time-of-flight secondary ion mass spectroscopy combined with focused ion beam technology to characterize the composition of a solid oxide fuel cell (SOFC) in three-dimension is demonstrated. The very high sensitivity of this method allows even very small amounts of elements/compounds to be detected and localized. Therefore, interlayer diffusion of elements between porous electrodes and presence of pollutants can be analyzed with a spatial resolution of the order of 100 nm. However, proper element recognition and mass interference still remain important issues. Here, we present a complete elemental analysis of the SOFC as well as techniques that help to validate the reliability of obtained results. A discussion on origins of probable artifacts is provided.

With the increasing miniaturization of electronic devices, high resolution structural and analytical characterization tools are necessary for the optimization of fabrication processes. Scanning transmission electron microscopy energy dispersive X‐ray (EDX‐STEM) spectroscopy is a well‐established technique that has recently gained momentum thanks to the introduction of high‐brightness electron sources and the Super‐X EDX system (4 SDD detectors), allowing fast EDX mapping with high collection efficiency. While the traditional EDX data analysis consists in extracting the elemental map of each element present in the sample [1], it is often the case that the aim of the analysis is to investigate the spatial distribution, shape and thickness of the different chemical phases present in the sample. Multivariate statistical analysis tools, such as non‐negative matrix factorization (NMF) and independent component analysis (ICA), were shown to yield simplified interpretation of spectral datasets by rapid identification of phases (e.g. [2,3]). In this work, we applied NMF to the EDX analysis of Si/SiGe multilayers to validate the Sidewall Image Transfer (SIT) process developed for their patterning [4]. A FIB‐prepared lamella was characterized in an FEI Titan Themis operating at 200kV and equipped with a probe corrector and 4 SDD EDX detectors. An EDX‐STEM map was acquired with TIA, using a pixel size of 1nm and a dwell time of 20ms/pixel, and exported to hyperspy, a python‐based software for hyperspectral data processing [5]. Spectral unmixing using NMF led to the identification of five chemical phases in the sample: Si, SiGe, SiO2, TiN and C (see the component spectra in Figure 1 and the corresponding loadings in Figure 2(a‐e)). More specifically, NMF succeeded in: (1) separating the Si signal emanating from pure Si, SiGe and SiO2 layers; and (2) deconvoluting the C, N and O peaks. This greatly simplified the compositional analysis (Figure 2(f)), and allowed a more straightforward estimation of the thickness of the different layers, as shown in Figure 2(g). NMF combined to EDX‐STEM tomography was recently applied to superalloy systems for aerospace applications [6,7]. We will show that this approach has also the potential to address materials characterization challenges currently facing the semiconductor industry, such as the chemical analysis of dopants and impurities [8].