IEICE TRANSACTIONS on Fundamentals
Neural Network Compensation for Frequency Cross-Talk in Laser Interferometry
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.2 pp.681-684
- Publication Date
- 2009/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.681
- Type of Manuscript
- LETTER
- Category
- Measurement Technology
Authors
Keyword
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Wooram LEE, Gunhaeng HEO, Kwanho YOU, "Neural Network Compensation for Frequency Cross-Talk in Laser Interferometry" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 2, pp. 681-684, February 2009, doi: 10.1587/transfun.E92.A.681.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.681/_p
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@ARTICLE{e92-a_2_681,
author={Wooram LEE, Gunhaeng HEO, Kwanho YOU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Neural Network Compensation for Frequency Cross-Talk in Laser Interferometry},
year={2009},
volume={E92-A},
number={2},
pages={681-684},
abstract={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.},
keywords={},
doi={10.1587/transfun.E92.A.681},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - Neural Network Compensation for Frequency Cross-Talk in Laser Interferometry
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 681
EP - 684
AU - Wooram LEE
AU - Gunhaeng HEO
AU - Kwanho YOU
PY - 2009
DO - 10.1587/transfun.E92.A.681
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2009
AB - 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.
ER -
English
