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Improving Scanning Electron Microscope Resolution for Near Planar Samples Through the Use of Image Restoration

Published online by Cambridge University Press:  14 October 2013

Eric Lifshin ,
Yudhishthir P. Kandel  and
Richard L. Moore
Eric Lifshin*
Affiliation:
College of Nanoscale Science and Engineering, University at Albany, State University of New York, 255 Fuller Rd, Albany, NY 12203, USA
Yudhishthir P. Kandel
Affiliation:
College of Nanoscale Science and Engineering, University at Albany, State University of New York, 255 Fuller Rd, Albany, NY 12203, USA
Richard L. Moore
Affiliation:
RLM2 Analytical, 29 Van Buren Street, Albany, NY 12206, USA
*
*Corresponding author. E-mail: elifshin@albany.edu
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Abstract

A method is presented for determining the point spread function (PSF) of an electron beam in a scanning electron microscope for the examination of near planar samples. Once measured, PSFs can be used with two or more low-resolution images of a selected area to create a high-resolution reconstructed image of that area. As an example, a 4× improvement in resolution for images is demonstrated for a fine gold particle sample. Since thermionic source instruments have high beam currents associated with large probe sizes, use of this approach implies that high-resolution images can be produced rapidly if the probe diameter is less of a limiting factor. Additionally, very accurate determination of the PSFs can lead to a better understanding of instrument performance as exemplified by very accurate measurement of the beam shape and therefore the degree of astigmatism.

Keywords

scanning electron microscopeimage restorationresolutiondeconvolutionastigmatism correctionPSF determination
Type
Techniques, Software, and Instrumentation Development
Information
Microscopy and Microanalysis , Volume 20 , Issue 1 , February 2014 , pp. 78 - 89
Copyright
Copyright © Microscopy Society of America 2014 

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References

Bertero, M. & Boccacci, P. (1998). Introduction to Inverse Problems in Imaging. Bristol, UK; Philadelphia, PA: Institute of Physics Pub.CrossRefGoogle Scholar
Castleman, K.R. (1996). Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Everhart, T.E. & Thornley, R.F.M. (1960). Wide-band detector for micro-microampere low-energy electron currents. J Sci Instr 37, 246.Google Scholar
Gold, Y.I. & Goldenshtein, A. (1998). SEM image sharpening by reversing the effect of non-ideal beam spot. SPIE 3332, 620–624.Google Scholar
Goldenshtein, A., Gold, Y.I. & Chayet, H. (1998). Measuring the size and intensity distribution of sem beam spot. SPIE 3332, 132–136.Google Scholar
Goldstein, J. (2003). Scanning Electron Microscopy and X-Ray Microanalysis. New York: Kluwer Academic/Plenum Publishers.Google Scholar
Hansen, P.C. (2006). Deblurring Images: Matrices, Spectra, and Filtering. Philadelphia, PA: Society for Industrial and Applied Mathematics.Google Scholar
Hansen, P.C. (2010). Discrete Inverse Problems: Insight and Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics.Google Scholar
Joy, D.C. (2002). SMART—A program to measure SEM resolution and imaging performance. J Microsc 208(Pt 1), 24–34.Google Scholar
Joy, D.C., Ko, Y.-U. & Hwu, J.J. (2000). Metrics of resolution and performance of CD-SEMs. Proc SPIE 3998 108, 108–114.Google Scholar
Kanaya, K. & Okayama, S. (1972). Penetration and energy-loss theory of electrons in solid targets. J Phys D: Appl Phys 5, 43.Google Scholar
Koshev, N.A., Luk'yanov, F.A., Rau, E.I., Sennov, R.A. & Yag, A.G. (2011). Increasing spatial resolution in the backscattered electron mode of scanning electron microscopy. Bull Russ Acad Sci, Phys 75(9), 1181–1184.Google Scholar
Liddle, J.A., Naulleau, P. & Schmidt, G. (2004). Probe shape measurements in an electron beam lithography system. J Vac Sci Technol B 22(6), 2897–2901.Google Scholar
Lifshin, E., Stessin, M. & Chagouel, I. (2012). Scanning Incremental Focus Microscopy. U.S. Patent Office. The Research Foundation of the State University of New York. Google Scholar
Lin, J. & Joy, D.C. (2005). A new examination of secondary electron yield data. Surf Interface Anal 37, 895–900.Google Scholar
Nakahira, K., Honda, T. & Miyamoto, A. (2012). Scanning Electron Microscope and Mehtod for Processing an Image Obtained by the Scanning Electron Microscope. U.S. Patent Office. Hitachi High-Technologies Corporation. US 8,106,357 B2. Google Scholar
Nakahira, K., Miyamoto, A. & Honda, T. (2008). A new image restoration technique for sem. Proc IFAC, Seoul, Korea, July 6–11, 2008, pp. 8185–8189. Google Scholar
Postek, M.T. & Vladar, A.E. (1998). Image sharpness measurement in scanning electron microsopy—Part I. Scanning 20, 1–9.Google Scholar
Reimer, L. (1998). Scanning Electron Microscopy: Physics of Image Formation and Microanalysis. Berlin, New York: Springer.Google Scholar
Rose, A. (1948). Television Pickup Tubes and the Problem of Vision. New York: Academic Press.Google Scholar
Sato, M. & Orloff, J. (1991). A method for calculating the current density of charged particle beams and the effect of finite source size and spherical and chromatic aberrations on focusing characteristics. J Vacuum Sci Technol B 9, 2602–2608.CrossRefGoogle Scholar
Sewell, P.B. & Ramachandran, K.N. (1977). A source imaging detector for the scanning electron microscope. Proc Tenth Ann Scanning Elec Microsc Symp, IIT Research Institute, Chicago, IL, March 1977, Vol. I, pp. 17–21. Google Scholar
Smith, S.W. (2003). Digital Signal Processing, a Practical Guide for Scientists and Engineers. Amsterdam: Elsevier Newnes.Google Scholar
Thurman, S.T. & Fienup, J.R. (2007). Noise histogram regularization for iterative image reconstruction algorithms. J Opt Soc Am A 24(3), 608–617.Google Scholar
Thurman, S.T. & Fienup, J.R. (2009). Wiener reconstruction of undersampled imagery. J Opt Soc Am A 26(2), 283–288.Google Scholar
Vanderlinde, W.E. & Caron, J.N. (2007). Blind deconvolution of SEM images. Proc 33 Int Symp Testing Failure Anal, San Jose, California, pp. 97–102. Google Scholar
Vladar, A.E., Postek, M.T. & Davidson, M.P. (1998). Image sharpness measurement in scanning microscopy—Part II. Scanning 20, 24–34.Google Scholar
Wells, O.C. (1977). Experimental method for measuring the electron-optical parameters of the scanning electron microscope (SEM). Proc Tenth Ann Scanning Elec Microsc Symp, IIT Research Institute, Chicago, IL, March 1977, Vol. I, pp. 25–32. Google Scholar
Yano, F. & Nomura, S. (1993). Deconvolution of scanning electron microscope images. Scanning 15, 19–24.Google Scholar