Application of PSO algorithm and RBF neural network in electrical impedance tomography

To measure the resistivity distribution of semiconductor wafers, this article applies electrical impedance tomography (EIT) technology to semiconductor resistivity measurements. A new method of Image reconstruction algorithm based on RBF neural network for EIT is proposed. The particle swarm optimiz...

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Bibliographic Details
Published in2009 9th International Conference on Electronic Measurement & Instruments pp. 2-517 - 2-521
Main Authors Peng Wang, Lili Xie, Yicai Sun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
Algorithm design and analysis
Conductivity measurement
connection weights adjustment
Design optimization
Electric variables measurement
electrical impedance tomography
Electrodes
Image reconstruction
Impedance measurement
Neural networks
Particle swarm optimization
RBF neural network
reconstruction image
Tomography
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Summary:To measure the resistivity distribution of semiconductor wafers, this article applies electrical impedance tomography (EIT) technology to semiconductor resistivity measurements. A new method of Image reconstruction algorithm based on RBF neural network for EIT is proposed. The particle swarm optimization algorithm (PSO) is designed to optimize the RBF network's connection weights. The simulation experiment results for 32 electrodes EIT data collecting system indicate that the PSO-RBF algorithm can improve the reconstruction image quality and the accuracy obviously, and that it is feasible of using RBF neural network to measure the resistivity distribution of semiconductor wafers.
ISBN:1424438632
9781424438631
DOI:10.1109/ICEMI.2009.5274525