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Analyzing Atomic Force Micrographs Using Spectral Methods

Published online by Cambridge University Press:  15 February 2011

Sameer D. Halepete ,
H. C. Lin ,
Simon J. Fang  and
C. R. Helms
Sameer D. Halepete
Affiliation:
Stanford University, CA 94305
H. C. Lin
Affiliation:
Stanford University, CA 94305
Simon J. Fang
Affiliation:
Stanford University, CA 94305
C. R. Helms
Affiliation:
Stanford University, CA 94305
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Abstract

Microroughness is a critical parameter in ULSI device interface reliability and has been shown to effect several critical MOS electrical properties. The atomic force microscope (AFM) has become the instrument of choice for silicon surface microroughness analysis. The parameters usually specified to characterize roughness are average and root mean square roughness. However, these parameters are spatial averages and can have the same value for two significantly different surfaces. Spectral analysis using the Fast Fourier Transform (FFT) has been applied as a powerful tool to analyze AFM data by looking at roughness as a function of spatial wavelength. The Fast Hartley Transform, being a real transform, is faster than the FFT and is better suited for this analysis. It has been used here to derive spectral information from the AFM height data. Before evaluating the transform, cancellation of any tilt or warp in the AFM data is done to remove frequency components which interfere with other spectral information. A PC-based computer program to determine the transform and its magnitude will be described. The application of this method to analyze data from Si and SiO2 surfaces as a function of pre-oxidation cleaning chemistry will be presented. Significantly better insight into the spatial distribution of roughness is obtained, when compared to previous implementations.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 386: Symposium O – Ultraclean Semiconductor Processing Technology and Surface , 1995 , 383
Copyright
Copyright © Materials Research Society 1995

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References

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