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Compound semiconductors belong to the most important materials for optoelectronic applications. Many of them exhibit favorable optical properties, such as a direct energy band gap (in contrast to silicon) and high-absorption coefficients over a wide spectral range. Moreover, varying the composition of the compound or substituting some of its elements often allows for controlled band gap engineering and optimization for specific applications. Because many compound semiconductors enable efficient conversion of light into electricity and vice versa, they are commonly used materials for optoelectronic devices.
We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.
The surface, or topmost layers of a material, is the region that is in contact with the environment. The composition and chemistry of the surface often can be drastically different from that of the bulk. For many materials systems (catalysts, coatings, biomedical devices, etc.), the surface chemistry and/or properties determine the device performance. Adhesion, delamination, staining, and corrosion are among the important surface phenomena that need to be understood in industrial settings. Over the past forty years, a number of surface analysis techniques have been commercialized to characterize the composition and microstructure of the surface. The most commonly used techniques are summarized in this issue of Microscopy Today.
Being able to differentiate surface from bulk defects on devices requires the use of complimentary characterization tools. In this article, we show how light microscopy, scanning electron microscopy, energy dispersive X-ray analysis, and time of flight secondary ion mass spectrometry provides complimentary information about the surface and sub-surface composition, topography, and microstructure of a semiconductor device.
To create a gamma-ray spectroscopy detector, electrical contacts consisting of a blanket coated cathode and a pixilated anode can be deposited directly on opposite faces of a cadmium zinc telluride (CZT) crystal. The contact metallization must adhere to the surfaces, and the streets between adjacent anode pads must be free of residual metal and contaminants to avoid excessive interpixel leakage currents. The analysis reported below was used to validate the structure and composition of the contact metal stack and to characterize the streets of the anode pad array.
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