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1
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The Investigation of Electron-Optical Parameters Using Artificial Neural Networks

100%
Isik A.
Acta Physica Polonica A
|
2015
|
vol. 127
|
issue 4
1317-1319
EN
The optimization of scientific instruments is crucially important to increase the quality of measurements. A major challenge for the development of these experimental tools is the precise determination of focal parameters. Therefore, usage of an innovative technique that meets our requirements is desirable. Among intelligent algorithms, artificial neural network (ANN) has an advantage of obtaining the optical parameters data with high accuracy. One of the most popular geometries used in electrostatic optical devices is geometry with cylinder lenses. In this study, the artificial neural network is applied for the first time to the subject of the magnification parameters of three-element electrostatic cylinder lenses for a wide range of values of the applied voltages. ANN-based optimization has been performed using Matlab/Simulink, and the performance analysis has also been conducted. High-performance results have been achieved using ANN approach. The commercial simulation package SIMION software is used as a data source for artificial neural network results. This approach provides new perspectives for the effective solution for the problems related to electrostatic lenses with different geometries.
2
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Prediction of Two-Element Cylindrical Electrostatic Lens Parameters Using Dynamic Artificial Neural Network

100%
Isik A.
Acta Physica Polonica A
|
2015
|
vol. 127
|
issue 6
1717-1722
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.
3
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Time Series Artificial Neural Network Approach for Prediction of Optical Lens Properties

63%
Isik A., Isik N.
Acta Physica Polonica A
|
2016
|
vol. 129
|
issue 4
514-516
EN
Well-designed electrostatic cylindrical lenses are commonly used to control charged particles in atomic and molecular physics instruments such as electron guns and electron microscopes. The most commonly used of these, three-element electrostatic lenses are capable of keeping magnification constant for definite image position. The correct determination of focal and aberration characteristics of these lenses is very important for experimental studies. In this study, motions of electrons in three-element electrostatic cylindrical lenses have been investigated with nonlinear autoregressive exogenous based time series artificial neural network technique. The spherical and chromatic aberrations which affect the beam are also predicted with time series artificial neural network technique. This method is a mathematical model that emulates the biological neural networks. The basic working principle of time series artificial neural network technique is training of network with the known data and then prediction of the unknown data. Simulation results from SIMION 8.1 ray-tracing program are used as training and test data set. According to the results obtained from time series artificial neural network technique technique, a considerably agreement is found between simulation and artificial neural network technique prediction results. The study shows that such an artificial neural network model which has time advantage can be applicable to various electron and ion beam apparatus.
4
Content available remote

Classification of Electron Gun Operation Modes Using Artificial Neural Networks

63%
Isik N., Isik A.
|
EN
In electron collision experiments, the seven-element electron gun is commonly used to accelerate and focus an electron beam. The main operation modes of this experimental device are afocal, zoom and broad beam-modes. Each of these operation modes can be used for producing electron beam with desired diameter. In this study, the artificial neural network classification technique (ANN) is used for classification of electron gun operation modes depending on electrostatic lens voltages. For this purpose, we investigate the focusing condition for the first three-element lens. Other ANN is employed for the second four-element lens voltages to find the electron gun operation modes. A comprehensive training data is obtained from SIMION software which uses traditional ray-tracing method. ANNs are trained with this dataset. Moreover, performance evaluations are carried out to determine the classification power of ANNs. High performance values show that the ANN can easily categorize the operation mode of the electron gun as a function of lens voltages. The proposed approach may help to adjust electron gun voltages before collision experiments. It is believed that this study will be a model for the future research in electron collision systems. Network can be trained with experimental data for practical applications.
5
Content available remote

Prediction of First Order Focusing Properties of Ideal Hemispherical Deflector Analyzer Using Artificial Neural Network

51%
Isik N., Isik A., Sise O., Guvenc U.
Acta Physica Polonica A
|
2017
|
vol. 131
|
issue 1
10-12
EN
Electrostatic energy analyzers are irreplaceable instruments to analyze the electron beams energies. In this context, the knowledge of electron trajectories in electrostatic energy analyzers has major importance in collision physics as well as in different scientific instruments for surface science. In this study, electron trajectories for different energies in an ideal field 180° hemispherical deflector analyzer are investigated by artificial neural network prediction method. The SIMION 8.1 simulation program is used as a data source for training and testing of artificial neural network. Artificial neural network based prediction has been performed using Matlab R2012b program. Obtained performance results indicate that this approach provides new perspectives for the rapid solution to the problems in charged particle optics.
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