Neural network approximation of nonlinearity in laser nano- metrology system based on TLMI
In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and nonorthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.
Saeed Olyaee Samaneh Hamedi
Nano-photonics and Optoelectronics Research Laboratory (NORLab), Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University (SRTTU), Lavizan, 16788, Tehran, Iran
国际会议
3rd International Photonics & OptoElectronics Meetings(第三届国际光子与光电子学会议 POEM 2010)
武汉
英文
1-8
2010-11-03（万方平台首次上网日期，不代表论文的发表时间）