Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-28T20:46:41.668Z Has data issue: false hasContentIssue false
  • English
  • Français

Exploring the Parameter Space of Point Spread Function Determination for the Scanning Electron Microscope—Part I: Effect on the Point Spread Function

Published online by Cambridge University Press:  27 August 2019

Mandy C. Nevins Open the ORCID record for Mandy C. Nevins [Opens in a new window] ,
Kathryn Quoi ,
Richard K. Hailstone  and
Eric Lifshin
Mandy C. Nevins
Affiliation:
Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA
Kathryn Quoi
Affiliation:
Nanoscience Constellation of the Colleges of Nanoscience and Engineering, SUNY Polytechnic Institute, Albany, NY 12203, USA
Richard K. Hailstone*
Affiliation:
Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA
Eric Lifshin
Affiliation:
Nanoscience Constellation of the Colleges of Nanoscience and Engineering, SUNY Polytechnic Institute, Albany, NY 12203, USA
*
*Author for correspondence: Richard Hailstone, E-mail: hailstone@cis.rit.edu
Get access

Abstract

The point spread function (PSF) of the scanning electron microscope (SEM) can be determined using a recently developed nanoparticle calibration method. Many parameters are involved in PSF determination and introduce a previously unstudied amount of uncertainty into the PSF size and shape. Signal type, support material thickness, reference particle size, PSF smoothing (K), and background correction were investigated regarding their effect on the PSF. Experimental data were complemented by CASINO simulations. Differences in detector position between the observed particles and the method's simulated reference particles caused shifting between secondary electron PSFs and backscattered electron PSFs. Support material thickness did not have a practical effect on the PSF at the tested voltages. Uncertainty in reference particle size varied the PSF full width at half maximum (FWHM) within ±0.7 nm at 2σ, with virtually no uncertainty in some cases. K and background correction within a reasonable range of values resulted in PSF FWHM differences within ±0.9 nm, except at 2 kV for K with an upper bound of ±1.9 nm due to increased noise. Tailoring K and background correction case-by-case would result in smaller differences. The interconnection of these parameters may help in future efforts to calculate their best selection.

Keywords

electron signal comparisonnanoparticlespoint spread functionscanning electron microscopyWiener filter
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 25 , Issue 5 , October 2019 , pp. 1167 - 1182
Copyright
Copyright © Microscopy Society of America 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Demers, J, Poirier-Demers, N, Couture, AR, Joly, D, Guilmain, M, de Jonge, N & Drouin, D (2011). Three-dimensional electron microscopy simulation with the CASINO Monte Carlo software. Scanning 33(3), 125–146.Google Scholar
Goldstein, J, Newbury, D, Michael, J, Ritchie, N, Scott, JH & Joy, D (2018). Scanning Electron Microscopy and X-Ray Microanalysis, 4th ed. New York, NY: Springer.Google Scholar
Gonzalez, RC & Woods, RE (Eds.) (2008). Image restoration and reconstruction. In Digital Image Processing, 3rd ed., pp. 311–393. Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
International Organization of Standardization (ISO) (2014). ISO 13322-1:2014—particle size analysis—image analysis methods—part 1: static image analysis methods. Available at www.iso.org.Google Scholar
Lifshin, E, Kandel, YP & Moore, RL (2014). Improving scanning electron microscope resolution for near planar samples through the use of image restoration. Microsc Microanal 20, 78–89.Google Scholar
Lifshin, E, Zotta, M, Frey, D, Lifshin, S, Nevins, M & Moskin, J (2017). A software approach to improving SEM resolution, image quality, and productivity. Microsc Today 3, 18–25.Google Scholar
Nevins, MC, Zotta, MD, Hailstone, RK & Lifshin, E (2017). Viability of point spread function deconvolution for SEM. Microsc Microanal 23(S1), 126–127.Google Scholar
Nevins, MC, Zotta, MD, Hailstone, RK & Lifshin, E (2018). Visualizing astigmatism in the SEM electron probe. Microsc Microanal 24(S1), 604–605.Google Scholar
Osawa, T, Yoshida, Y, Tsuzuku, F, Nozaka, M, Takashio, M & Nozaka, Y (1999). The advantage of the osmium conductive metal coating for the detection of the colloidal gold-conjugated antibody by SEM. J Electron Microsc 48(5), 665–669.Google Scholar
Otsu, N (1979). A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern 9(1), 62–66.Google Scholar
Postek, MT, Vladár, AE, Villarrubia, JS & Muto, A (2016). Comparison of electron imaging modes for dimensional measurements in the scanning electron microscope. Microsc Microanal 22(4), 768–777.Google Scholar
Reimer, L (1998). Scanning Electron Microscopy: Physics of Image Formation and Microanalysis, 2nd ed. Berlin/Heidelberg: Springer-Verlag.Google Scholar
Zotta, MD (2016). An examination of experimental factors affecting point spread function (PSF) determination in a scanning electron microscope (SEM). Master's Thesis. College of Nanoscale Science and Engineering, Albany, NY.Google Scholar
Zotta, MD, Nevins, MC, Hailstone, H & Lifshin, E (2018). The determination and application of the point spread function in the scanning electron microscope. Microsc Microanal 24(4), 396–405.Google Scholar
Supplementary material: File

Nevins et al. supplementary material

Nevins et al. supplementary material 1

Download Nevins et al. supplementary material(File)
File 2 MB