Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-04T21:03:33.615Z Has data issue: false hasContentIssue false
  • English
  • Français

Exploring the Parameter Space of Point Spread Function Determination for the Scanning Electron Microscope—Part II: Effect on Image Restoration Quality

Published online by Cambridge University Press:  30 August 2019

Mandy C. Nevins Open the ORCID record for Mandy C. Nevins [Opens in a new window] ,
Richard K. Hailstone  and
Eric Lifshin
Mandy C. Nevins
Affiliation:
Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, 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 K. Hailstone, E-mail: [email protected]
Get access

Abstract

Point spread function (PSF) deconvolution is an attractive software-based technique for resolution improvement in the scanning electron microscope (SEM) because it can restore information which has been blurred by challenging operating conditions. In Part 1, we studied a modern PSF determination method for SEM and explored how various parameters affected the method's ability to accurately estimate the PSF. In Part 2, we extend this exploration to PSF deconvolution for image restoration. The parameters include reference particle size, PSF smoothing (K), background correction, and restoration denoising (λ). Image quality was assessed by visual inspection and Fourier analysis. Overall, PSF deconvolution improved image quality. Low λ enhanced image sharpness at the cost of noise, while high λ created smoother restorations with less detail. λ should be chosen to balance feature preservation and denoising based on the application. Reference particle size within ±0.9 nm and K within a reasonable range had little effect on restoration quality. Restorations using background-corrected PSFs had superior quality compared with using no background correction, but if the correction was too high, the PSF was cut off causing blurrier restorations. Future efforts to automatically determine parameters would remove user guesswork, improve this method's consistency, and maximize interpretability of outputs.

Keywords

deconvolutionimage restorationlow voltagepoint spread functionscanning electron microscopy
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 25 , Issue 5 , October 2019 , pp. 1183 - 1194
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

Goldstein, J, Newbury, D, Michael, J, Ritchie, N, Scott, JH & Joy, D (2018). Scanning Electron Microscopy and X-ray Microanalysis, 4th ed. New York: 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 for Standardization (ISO) (2011). ISO/TS 245597:201 1(E)—Microbeam Analysis—Scanning Electron Microscopy—Methods of Evaluating Image Sharpness. Available at www.iso.org.Google Scholar
Joy, DC, Ko, YU & Hwu, JJ (2000). Metrics of resolution for CD-SEMs. In Metrology, Inspection, and Process Control for Microlithography XIV, Proceedings of the SPIE, vol. 3998.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
Lucy, LB (1974). An iterative technique for the rectification of observed distributions. Astron J 79(6), 745–754.Google Scholar
Richardson, WH (1972). Bayesian-based iterative method of image restoration. J Opt Soc Am 62(1), 55–59.Google Scholar
Roels, J, Aelterman, J, Luong, HQ, Lippens, S, Pižurca, A, Saeys, Y & Philips, W (2018). An overview of state-of-the-art image restoration in electron microscopy. J Microsc 271(3), 239–254.Google Scholar
Rudin, LI, Osher, S & Fatemi, E (1992). Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268.Google Scholar
Wang, Z, Bovik, AC, Sheikh, HR & Simoncelli, EP (2004). Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13(4), 600–612.Google Scholar
Zotta, MD, Nevins, MC, Hailstone, RK & 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

Download Nevins et al. supplementary material(File)
File 7.9 MB