Recent years have seen the introduction of many surface characterization instruments and other spectral imaging systems that are capable of generating data in truly prodigious quantities. The challenge faced by the analyst, then, is to extract the essential chemical information from this overwhelming volume of spectral data. Multivariate statistical techniques such as principal component analysis (PCA) and other forms of factor analysis promise to be among the most important and powerful tools for accomplishing this task. In order to benefit fully from multivariate methods, the nature of the noise specific to each measurement technique must be taken into account. For spectroscopic techniques that rely upon counting particles (photons, electrons, etc.), the observed noise is typically dominated by 'counting statistics' and is Poisson in nature. This implies that the absolute uncertainty in any given data point is not constant, rather, it increases with the number of counts represented by that point. Performing PCA, for instance, directly on the raw data leads to less than satisfactory results in such cases. This paper will present a simple method for weighting the data to account for Poisson noise. Using a simple time-of-flight secondary ion mass spectrometry spectrum image as an example, it will be demonstrated that PCA, when applied to the weighted data, leads to results that are more interpretable, provide greater noise rejection and are more robust than standard PCA. The weighting presented here is also shown to be an optimal approach to scaling data as a pretreatment prior to multivariate statistical analysis.

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

Real-time measurements of stress evolution during the deposition of VolmerWeber thin films reveal a complex interplay between mechanisms for stress generation and stress relaxation. We observed a generic stress evolution from compressive to tensile, then back to compressive stress as the film thickened, in amorphous and polycrystalline Ge and Si, as well as in polycrystall;ne Ag, Al, and Ti. Direct measurements of stress relaxation during growth interrupts demonstrate that the generic behavior can occur even in the absence of stress relaxation. When relaxation did occur, the mechanism depended sensitively on whether the film was continuous or discontinuous, on the process conditions, and on the fildsubstrate interracial strength.For Ag films, interracial shear dominated the early relaxation behnavior, whereas this mechanism was negligible in Al films due to the much stronger bonding at the A1/SiOz interface. For amorphous Ge, selective relaxation of tensile stress was observed only at elevated temperatures, consistent with surface-diffusion-based mechanisms. In "all the films studied here, stress relaxation was suppressed after the films became continuous...

High‐performance memristors based on AlN films have been demonstrated, which exhibit ultrafast ON/OFF switching times (≈85 ps for microdevices with waveguide) and relatively low switching current (≈15 μA for 50 nm devices). Physical characterizations are carried out to understand the device switching mechanism, and rationalize speed and energy performance. The formation of an Al‐rich conduction channel through the AlN layer is revealed. The motion of positively charged nitrogen vacancies is likely responsible for the observed switching.

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