Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-05-06T12:54:31.626Z Has data issue: false hasContentIssue false
  • English
  • Français

Determination of Electron Optical Properties for Aperture Zoom Lenses Using an Artificial Neural Network Method

Published online by Cambridge University Press:  16 February 2016

Nimet Isik
Nimet Isik*
Affiliation:
Department of Science Education, Mehmet Akif Ersoy University, 15200 Burdur, Turkey
*
*Corresponding author. nimetok@hotmail.com
Get access

Abstract

Multi-element electrostatic aperture lens systems are widely used to control electron or charged particle beams in many scientific instruments. By means of applied voltages, these lens systems can be operated for different purposes. In this context, numerous methods have been performed to calculate focal properties of these lenses. In this study, an artificial neural network (ANN) classification method is utilized to determine the focused/unfocused charged particle beam in the image point as a function of lens voltages for multi-element electrostatic aperture lenses. A data set for training and testing of ANN is taken from the SIMION 8.1 simulation program, which is a well known and proven accuracy program in charged particle optics. Mean squared error results of this study indicate that the ANN classification method provides notable performance characteristics for electrostatic aperture zoom lenses.

Keywords

electron opticselectrostatic lenszoom lensartificial neural networkoptimization
Type
Materials Applications
Information
Microscopy and Microanalysis , Volume 22 , Issue 2 , April 2016 , pp. 458 - 462
Copyright
© Microscopy Society of America 2016 

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

Benis, E.P. & Zouros, T.J.M. (2008). The hemispherical deflector analyser revisited: II. Electron-optical properties. J Electron Spectros Relat Phenomena 163, 28–39.CrossRefGoogle Scholar
Boesten, L. & Okada, K. (2000). Hairpin-filament electron guns for low-energy use. Meas Sci Technol 11, 576–583.Google Scholar
Cubric, D., Lencova, B. & Read, F.H. (1999). Comparison of FDM, FEM and BEM for electrostatic charged particle optics. Nucl Instrum Methods Phys Res A 427, 357–362.Google Scholar
Dahl, D.A., Delmore, J.E. & Appelhans, A.D. (1990). SIMION PC/PS2 electrostatic lens design program. Rev Sci Instrum 61, 607–609.Google Scholar
Harting, E. & Read, F.H. (1976). Electrostatic Lenses. Amsterdam: Elsevier.Google Scholar
Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Englewood Cliffs, NJ: Prentice-Hall, Inc.Google Scholar
Heddle, D.W.O. (2000). Electrostatic Lens Systems. London: IOP Press.Google Scholar
Imhof, R.E. & Read, F.H. (1968). A three-aperture electron optical lens for producing an image of variable energy but fixed position. J Phys E Sci Instrum 1, 859–860.Google Scholar
Isik, A.H. (2015 a). The investigation of electron-optical parameters using artificial neural networks. Acta Phys Pol A 127, 1317–1319.Google Scholar
Isik, A.H. (2015 b). Prediction of two-element cylindrical electrostatic lens parameters using dynamic artificial neural network Acta Phys Pol A 127, 1717–1721.Google Scholar
Kuyatt, C.J. & Simpson, J.A. (1967). Electron monochromator design. Rev Sci Instrum 38, 103–111.CrossRefGoogle Scholar
Lencova, B. (1995). Computation of electrostatic lenses and multipoles by the first order finite element method. Nucl Instrum Methods Phys Res A 363, 190–197.Google Scholar
Lippman, R.P. (1987). An introduction to computing with neural nets. ASSP Mag IEEE 4, 4–22.Google Scholar
Moller, M.F. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6, 525–533.CrossRefGoogle Scholar
Munro, E. (1988). Finite difference programs for computing tolerances for electrostatic lenses. J Vac Sci Technol B 6, 941–948.CrossRefGoogle Scholar
Read, F.H. (1969). Accurate calculations of double-aperture electrostatic immersion lenses. J Phys E Sci Instrum 2, 165–169.Google Scholar
Read, F.H. (1970). A symmetric electrostatic lenses of three apertures. J Phys E Sci Instrum 3, 127–131.Google Scholar
Read, F.H. (1983). A “movable” electrostatic lens. J Phys E Sci Instrum 16, 636–642.CrossRefGoogle Scholar
Read, F.H. (2011). The CPO programs and the BEM for charged particle optics. Nucl Instrum Methods Phys Res A 645, 273–277.Google Scholar
Riddle, G.N.H. (1978). Electrostatic einzel lenses with reduced spherical aberration for use in field emission guns. J Vac Sci Technol 15, 857–860.CrossRefGoogle Scholar
Shimizu, K. & Kawakatsu, H. (1974). Design charts of electrostatic three-aperture lenses. J Phys E Sci Instrum 7, 472–476.Google Scholar
Sise, O., Okumus, N., Ulu, M. & Dogan, M. (2009). Computer simulation of electrostatic aperture lens systems for electron spectroscopy. J Electron Spectros Relat Phenomena 175, 76–86.CrossRefGoogle Scholar
Sise, O., Ulu, M. & Dogan, M. (2005). Multi-element cylindrical electrostatic lens systems for focusing and controlling charged particles. Nucl Instrum Methods Phys Res A 554, 114–131.Google Scholar
Trager-Cowan, C., Miller, R.B., Kay, S.M. & Heddle, D.W.O. (1990). Multiple-element electrostatic lenses: II. A multiple disc lens. Meas Sci Technol 1, 747–750.Google Scholar