Abstract/Summary

This work discusses the detection of small compositional changes in materials that have microstructures containing conducting and dielectric phases, which can be described by networks of resistive (R) and capacitive (C) components in a three-dimensional lattice. For this purpose, a principal component analysis (PCA) method is employed to discriminate normal samples from samples with altered composition on the basis of statistics extracted from the waveform of the network response to a given excitation. This approach obviates the requirement for multivariate regression and simplifies experimental workload for model-building, since only data from normal samples are required in the development of the PCA model. Waveform variability of the excitation source is also accounted for through the use of a nominal model derived using subspace identification. This enables standardization and software based metrology transfer across different labs. The effect of network size on the capability of detecting minute compositional changes was assessed. For networks of 520 components, it was possible to identify changes in the fraction of capacitors down to 10-2 at 2 sigma confidence levels.