Treffer: Application of BP-neural networks in the FOCAL technique

Title:
Application of BP-neural networks in the FOCAL technique
Authors:
WEIJIE SHI, XIANGZHAO WANG, DONGQING ZHANG, FAN WANG, MINGYING MA
Source:
Advanced microlithography technologies (Beijing, 8-10 November 2004)SPIE proceedings series. :233-239
Publisher Information:
Bellingham WA: SPIE, 2005.
Publication Year:
2005
Physical Description:
print, 11 ref
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Optics, Optique, Physics, Physique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Electronique, Electronics, Electronique des semiconducteurs. Microélectronique. Optoélectronique. Dispositifs à l'état solide, Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices, Fabrication microélectronique (technologie des matériaux et des surfaces), Microelectronic fabrication (materials and surfaces technology), Circuits électriques, optiques et optoélectroniques, Electric, optical and optoelectronic circuits, Réseaux neuronaux, Neural networks, Ajustement courbe, Curve fitting, Ajustamiento curva, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Apprentissage, Learning, Aprendizaje, Astigmatisme, Astigmatism, Astigmatismo, Défaillance, Failures, Fallo, Fabrication microélectronique, Microelectronic fabrication, Fabricación microeléctrica, Formation image, Imaging, Formación imagen, Lithographie, Lithography, Litografía, Méthode mesure, Measurement method, Método medida, Méthode moindre carré, Least squares method, Método cuadrado menor, Pastille électronique, Wafer, Pastilla electrónica, Performance algorithme, Algorithm performance, Resultado algoritmo, Précision mesure, Measurement accuracy, Precisión medida, Réseau neuronal, Neural network, Red neuronal
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Graduate School of the Chinese Academy of Sciences, Beijing 100039, China
Information Optics Laboratory, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17046289
Rights:
Copyright 2005 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Electronics
Accession Number:
edscal.17046289
Database:
PASCAL Archive

Weitere Informationen

FOCAL is an on-line measurement technique of the imaging parameters of a lithographic tool with high accuracy. These parameters include field curvature, astigmatism, best focus and image tilt. They can be acquired by the least-square algorithm from the alignment positions of the special marks on the exposed wafer. But the algorithm has some intrinsic limits which may lead to a failure of the curve fitting. This will influence the measurement accuracy of the imaging parameters obtained by FOCAL. Therefore, a more reliable algorithm for the FOCAL technique is needed. In this paper, the feed-forward back-propagation artificial neural network algorithm is introduced in the FOCAL technique, and the FOCAL technique based on BP ANN is proposed. The effects of the parameters, such as the number of neurons on the hidden-layer, the number of training epochs, on the measurement accuracy are analyzed in detail. It is proved that the FOCAL technique based on BP-ANN is more reliable and it is a better choice for measurement of the imaging parameters.