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Proceedings Paper

Comparison of the ability of quantitative parameters to differentiate surface texture of Atomic Force Microscope (AFM) images
Author(s): Bethany Niedzielski; Christine Caragianis Broadbridge; John S. DaPonte; Maria Gherasimova
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Paper Abstract

The purpose of this study was to compare the ability of several texture analysis parameters to differentiate textured samples from a smooth control on images obtained with an Atomic Force Microscope (AFM). Surface roughness plays a major role in the realm of material science, especially in integrated electronic devices. As these devices become smaller and smaller, new materials with better electrical properties are needed. New materials with smoother surface morphology have been found to have superior electrical properties than their rougher counterparts. Therefore, in many cases surface texture is indicative of the electrical properties that material will have. Physical vapor deposition techniques such as Jet Vapor Deposition and Molecular Beam Epitaxy are being utilized to synthesize these materials as they have been found to create pure and uniform thin layers. For the current study, growth parameters were varied to produce a spectrum of textured samples. The focus of this study was the image processing techniques associated with quantifying surface texture. As a result of the limited sample size, there was no attempt to draw conclusions about specimen processing methods. The samples were imaged using an AFM in tapping mode. In the process of collecting images, it was discovered that roughness data was much better depicted in the microscope's "height" mode as opposed to "equal area" mode. The AFM quantified the surface texture of each image by returning RMS roughness and the first order histogram statistics of mean roughness, standard deviation, skewness, and kurtosis. Color images from the AFM were then processed on an off line computer running NIH ImageJ with an image texture plug in. This plug in produced another set of first order statistics computed from each images' histogram as well as second order statistics computed from each images' cooccurrence matrix. The second order statistics, which were originally proposed by Haralick, include contrast, angular second moment, correlation, inverse difference moment, and entropy. These features were computed in the 0°, 45°, 90°, and 135° directions. The findings of this study propose that the best combination of quantitative texture parameters is standard deviation, 0° inverse difference moment, and 0° entropy, all of which are obtained from the NIH ImageJ texture plug in.

Paper Details

Date Published: 29 January 2010
PDF: 12 pages
Proc. SPIE 7538, Image Processing: Machine Vision Applications III, 75380B (29 January 2010); doi: 10.1117/12.838850
Show Author Affiliations
Bethany Niedzielski, Rensselaer Polytechnic Institute (United States)
Christine Caragianis Broadbridge, Southern Connecticut State Univ. (United States)
John S. DaPonte, Southern Connecticut State Univ. (United States)
Maria Gherasimova, Southern Connecticut State Univ. (United States)


Published in SPIE Proceedings Vol. 7538:
Image Processing: Machine Vision Applications III
David Fofi; Kurt S. Niel, Editor(s)

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