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Improving AFM Images with Harmonic Interference by Spectral Analysis

Published online by Cambridge University Press:  04 January 2012

Marek Kiwilszo*
Affiliation:
Faculty of Electronics, Telecommunications and Informatics, Department of Optoelectronics and Electronics Systems, Gdańsk University of Technology, Narutowicza Str. 11/12, 80-233 Gdańsk, Poland
Artur Zieliński
Affiliation:
Chemical Faculty, Department of Electrochemistry, Corrosion and Material Engineering, Gdańsk University of Technology, Narutowicza Str. 11/12, 80-233 Gdańsk, Poland
Janusz Smulko
Affiliation:
Faculty of Electronics, Telecommunications and Informatics, Department of Optoelectronics and Electronics Systems, Gdańsk University of Technology, Narutowicza Str. 11/12, 80-233 Gdańsk, Poland
Kazimierz Darowicki
Affiliation:
Chemical Faculty, Department of Electrochemistry, Corrosion and Material Engineering, Gdańsk University of Technology, Narutowicza Str. 11/12, 80-233 Gdańsk, Poland
*
Corresponding author. E-mail: Marek.Kiwilszo@gmail.com
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Abstract

Atomic force microscopy (AFM) is one of the most sensitive tools for nanoscale imaging. As such, it is very sensitive to external noise sources that can affect the quality of collected data. The intensity of the disturbance depends on the noise source and the mode of operation. In some cases, the internal noise from commercial AFM controllers can be significant and difficult to remove. Thus, a new method based on spectrum analysis of the scanned images is proposed to reduce harmonic disturbances. The proposal is a post-processing method and can be applied at any time after measurements. This article includes a few methods of harmonic cancellation (e.g., median filtering, wavelet denoising, Savitzky-Golay smoothing) and compares their effectiveness. The proposed method, based on Fourier transform of the scanned images, was more productive than the other methods mentioned before. The presented data were achieved for images of conductive layers taken in a contact AFM mode.

Type
Techniques Development
Copyright
Copyright © Microscopy Society of America 2012

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References

REFERENCES

Binnig, G., Quate, C.F. & Gerber, Ch. (1986). Atomic force microscope. Phys Rev Lett 56, 930933.CrossRefGoogle ScholarPubMed
Ciureanu, M. & Wang, H. (2000). Electrochemical impedance study of anode CO-poisoning in PEM fuel cells. J New Mat Electrochem Syst 3, 107119.Google Scholar
Dengwen, Z. (2007). An image denoising algorithm with an adaptive window. Proceedings of the IEEE International Conference on Image Processing, San Antonio, Texas, September 16–19, 2007, pp. I-333–I-336.CrossRefGoogle Scholar
Eaton, P. & West, P. (2010). AFM image artifacts. Atomic Force Microsc 19, 121139.Google Scholar
Fleig, J. (2002). The grain boundary impedance of random microstructures: Numerical simulations and implications for the analysis of experimental data. Solid State Ionics 150, 181193.CrossRefGoogle Scholar
Göken, M. & Vehoff, H. (1996). Quantitative metallography of structural materials with the atomic force microscope. Scripta Mater 35, 983989.CrossRefGoogle Scholar
Koay, S.Y., Ramli, A.R., Lew, Y.P., Prakash, V. & Ali, R. (2002). A motion region estimation technique for web camera applications. SCOReD Student Conference on Research and Development, Shah Alam, July 16–17, 2002, pp. 352–355.CrossRefGoogle Scholar
Langer, M.G., Koitschev, A., Haase, H., Rexhausen, U., Horber, J.K. & Ruppersberg, J. P. (2000). Mechanical stimulation of individual stereocilia of living cochlear hair cells by atomic force microscopy. Ultramicroscopy 82, 269278.Google Scholar
Lee, G., Jung, H., Son, J., Nam, K., Kwon, T., Lim, G., Ho Kim, Y., Seo, J., Woo Lee, S. & Sung Yoon, D. (2010). Experimental and numerical study of electrochemical nanomachining using an AFM cantilever tip. Nanotechnology 21, 185301.Google Scholar
Liu, C., Szeliski, R., Kang, S.B., Zitnick, C.L. & Freeman, W.T. (2008). Automatic estimation and removal of noise from a single image. IEEE T Pattern Anal 30, 299314.CrossRefGoogle ScholarPubMed
Marsh, T.C., Vesenka, J. & Henderson, E. (1995). A new DNA nanostructure, the G-wire, imaged by scanning probe microscopy. Nucleic Acids Res 23, 696700.CrossRefGoogle ScholarPubMed
Méndez-Vilas, A., González-Martín, M.L. & Nuevo, M.J. (2002). Optical interference artifacts in contact atomic force microscopy images. Ultramicroscopy 92, 243250.Google Scholar
Mohideen, U. & Roy, A. (1998). Precision measurement of the Casimir force from 0.1 to 0.9 μm. Phys Rev Lett 81, 45494552.CrossRefGoogle Scholar
O'Hayre, R., Lee, M. & Prinz, F.B. (2004). Ionic and electronic impedance imaging using atomic force microscopy. J Appl Phys 95, 83828392.Google Scholar
Popkirov, G.S. & Barsoukov, E. (1995). In-situ impedance spectra investigation during oxidation and reduction of conductive polymers. Significance of the capacitive currents. J Electroanal Chem 383, 155160.Google Scholar
Press, W.H., Teukolsky, S.A., Vetterling, W.T. & Flannery, B.P. (2007). Numerical Recipes—The Art of Scientific Computing. New York: Cambridge University Press.Google Scholar
Protter, M. & Elad, M. (2009). Image sequence denoising via sparse and redundant representations. IEEE T Image Process 18, 2935.CrossRefGoogle ScholarPubMed
Saha, B., Liu, E., Tor, S.B., Khun, N.W., Hardt, D.E. & Chun, J.H. (2010). Replication performance of Si-N-DLC-coated Si micro-molds in micro-hot-embossing. J Micromech Microeng 20, 045007.CrossRefGoogle Scholar
Shim, Y.B., Won, M.S. & Park, S.M. (1990). In-situ spectroelectrochemical studies of polyaniline growth mechanisms. J Electrochem Soc 137, 538544.Google Scholar
Stark, R.W., Rubio-Sierra, F.J., Thalhammer, S. & Heckl, W.M. (2003). Combining mechanical manipulation by atomic force microscopy with UV-laser micro beam manipulation. Eur Biophys J 32, 3339.CrossRefGoogle Scholar
Thornley, D.J. (2006). Anisotropic Multidimensional Savitzky-Golay Kernels for Smoothing, Differentiation and Reconstruction. London: Imperial College London.Google Scholar
West, P. & Starostina, N. (n.d.). A Guide to AFM Image Artifacts. Santa Clara, CA: Pacific Nanotechnology, Inc.Google Scholar
Westra, K.L., Mitchell, A.W. & Thomson, D.J. (1993). Tip artifacts in atomic force microscope imaging of thin film surfaces. J Appl Phys 74, 36083610.CrossRefGoogle Scholar
Wilson, D.I. (2006). The black art of smoothing, Electrical & Automation Technology June/July, 35–36.Google Scholar