Neural Network Compensation for Frequency Cross-Talk in Laser Interferometry

Wooram LEE
Gunhaeng HEO
Kwanho YOU

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E92-A    No.2    pp.681-684
Publication Date: 2009/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E92.A.681
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Measurement Technology
laser interferometry,  nonlinearity compensation,  frequency cross-talk,  neural network,  

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The heterodyne laser interferometer acts as an ultra-precise measurement apparatus in semiconductor manufacture. However the periodical nonlinearity property caused from frequency cross-talk is an obstacle to improve the high measurement accuracy in nanometer scale. In order to minimize the nonlinearity error of the heterodyne interferometer, we propose a frequency cross-talk compensation algorithm using an artificial intelligence method. The feedforward neural network trained by back-propagation compensates the nonlinearity error and regulates to minimize the difference with the reference signal. With some experimental results, the improved accuracy is proved through comparison with the position value from a capacitive displacement sensor.

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