Pa. Amato et al., QUANTITATIVE IMAGE-ANALYSIS OF SUPERCONDUCTOR THIN-FILM MICROSTRUCTURE - THE USE OF CONDITIONAL, MULTIPARAMETRIC, SHAPE-ANALYSIS ALGORITHMS, Journal of materials research, 8(11), 1993, pp. 2799-2809
We have developed a novel approach for quantifying the microstructure
of granular thin films using digital image processing and analysis. In
the past, conventional scanning electron microscopy of thin films has
generated qualitative information on the surface topography and film
microstructure. However, when coupled to digital image analysis, the a
mount or degree of surface contours (i.e., granularity) in SEM microgr
aphs can be quantified in a rapid and reproducible manner. Briefly, SE
M micrographs are digitized and the edge boundaries on the film surfac
e are enhanced by a gradient filter; granularity is then quantified by
calculating the %AREA covered by the edges with respect to the entire
field. Objects of a particular shape, such as phase impurity particle
s, can be selectively deleted from the image using a specific sequence
of shape analysis algorithms and parameter values. In this manner, th
e contributions of edges from the phase impurity particles is minimize
d in the final measurement of real surface contours. Statistical analy
sis of the data yields quantitative information concerning variations
in microdomains within single thin films and can detect statistically
significant differences among samples. This method is being used in th
e characterization of the microstructure of superconducting thin films
for optimization of their electrical and magnetic properties.