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Modeling and Compensation of Periodic Nonlinearity in Two-mode Interferometer Using Neural Networks

Author(s): Olyaee Saeed | Ebrahimpour Reza | Hamedi Samaneh

Journal: IETE Journal of Research
ISSN 0377-2063

Volume: 56;
Issue: 2;
Start page: 102;
Date: 2010;
Original page

Keywords: Ensemble of neural networks | Heterodyne interferometer | Multi-layer perceptrons | Nonlinearity reduction | Stacked generalization.

The periodic nonlinearity in nano-metrology systems based on heterodyne interferometers is the most important limitation to the accuracy of displacement measurement. It is mainly produced due to the polarization-mixing and frequency-mixing. In this paper, a new approach based on an ensemble of neural networks for modeling and compensation of nonlinearity in a high-resolution laser heterodyne interferometer is presented. We model the periodic nonlinearity arising from elliptical polarization and non-orthogonality of the laser polarized light based on the neural network approaches, including the multi-layer perceptrons and radial basis function as single neural networks and stacked generalization method as ensemble of neural networks. It is also shown that by using the stacked generalization method, the primary periodic nonlinearity of 1.3 nm is significantly compensated by a factor of 168.
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