Detecting compositional changes in dielectric materials simulated by three-dimensional RC network models

摘要: 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 resistive (R) capacitive (C) components a three-dimensional lattice. For this purpose, principal component analysis (PCA) method is employed to discriminate normal samples from with altered composition on basis statistics extracted waveform network response given excitation. approach obviates requirement for multivariate regression simplifies experimental workload model-building, since only data are required development PCA model. Waveform variability excitation source also accounted through use nominal model derived using subspace identification. enables standardization software based metrology transfer across different labs. The effect size capability detecting minute was assessed. 520 components, it possible identify fraction capacitors down 10−2 at ±2σ confidence levels.