Prediction of Two-Element Cylindrical Electrostatic Lens Parameters Using Dynamic Artificial Neural Network

• Authors: A. Isik
• Languages of publication: EN

Abstracts

EN

Two-element cylindrical electrostatic lens systems allowed to control low energy electron or charged particle beam have great importance. In this context, dynamic artificial neural network using nonlinear autoregressive exogenous model has been utilized to predict optimum linear magnification and overall voltage values for these lens designs. The focusing characteristics of electron beam in two-element cylindrical lens systems are investigated with two different nonlinear autoregressive exogenous based artificial neural network models. First artificial neural network model is employed for predicting of voltage ratios of lenses and magnification values. This model interpolates among the object (P) and image positions (Q) and finally finds optimum voltage ratios and magnification values through training dataset. Due to the deviations of electron trajectories in a real lens system, the spherical aberration effects are also taken into account to determine the optimal lens parameters. Therefore, the second artificial neural network model is constructed for predicting spherical aberration coefficients in image point. For each of artificial neural network models, training, test and validation data set are obtained from SIMION 8.1 ion and electron optics software. Artificial neural network model outputs are compared with the SIMION data and very good agreements are found. While artificial neural network is frequently applied in different fields, this is the first study that uses dynamic artificial neural network to predict the parameters of electrostatic lens. It is believed that this pioneering work will be a guide for the future investigations in lens design systems.

Keywords

EN

84.35.+i; 07.05.Mh; 41.85.Ne

Discipline

• 84.35.+i: Neural networks(for optical neural networks, see 42.79.Ta; see also 07.05.Mh Neural networks, fuzzy logic, artificial intelligence in computers in experimental physics; 87.18.Sn in biological complexity)
• 41.85.Ne: Electrostatic lenses, septa
• 07.05.Mh: Neural networks, fuzzy logic, artificial intelligence

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2015
• Volume: 127
• Issue: 6
• Pages: 1717-1722

Dates

published

2015-06

received

2014-08-13

(unknown)

2015-01-25

Contributors

author

A. Isik

• Department of Computer Engineering, Mehmet Akif Ersoy University, Burdur, Turkey

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Identifiers

DOI

10.12693/APhysPolA.127.1717