Contact free potential mapping by vibrating capacitor
The contact free vibrating capacitor method is a valuable tool for investigating the surface potentials of solid surfaces. The purpose of the present article is to summarize the theory and capabilities of the vibrating capacitor method, especially scanning vibrating capacitor pictures in the electronics. After a brief review some results will be discussed, such as a contact potential map taken from a printed circuit board, a surface potential map from a biased ceramic thick film circuit and some results from silicon solid state devices and solar cells. Potential maps contain a lot of information concerning the surface conditions, included the inhomogeneities of the technology, bias and other additional excitation. These potential maps may help in the development, quality control and defect analysis
Vibrating capacitor method in the development of semiconductor gas sensors
Abstract Adsorption usually results in work function shifts on catalytically active surfaces such as semiconductor gas sensors. The purpose of the present article is to summarise the capabilities of the vibrating capacitor from the simplest adsorption-induced work function tests to the scanning, vibrating, capacitor-yielded olfactory pictures and other chemical pictures. After a brief history and review of theoretical bases, the latest results will be discussed in detail. Olfactory pictures from semiconductor surfaces give a new chance to improve the selectivity of gas analysis. Chemical pictures from thin SnO 2 layers produced by atomic layer epitaxy reveal the inhomogeneities of the technology. CPD maps taken from Pd nanolayer (activator)-covered surfaces help to find the best layer-depositing parameters for the activation process of the thin semiconductor gas sensor films.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.