A novel key-variable sifting algorithm for virtual metrology

Tung Ho Lin, Fan-Tien Cheng, Aeo Juo Ye, Wei Ming Wu, Min Hsiung Hung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This work proposes an advanced key-variable selecting method, the neural-network-based stepwise selection (NN-based SS) method, which can enhance the conjecture accuracy of the NN-based virtual metrology (VM) algorithms. Multi-regression-based (MR-based) SS method is widely applied in dealing with key-variable selecting problems despite that it may not guarantee finding the best model based on its selected variables. However, the variables selected by MR-based SS may be adopted as the initial set of variables for the proposed NN-based SS to reduce the SS process time. The backward elimination and forward selection procedures of the proposed NN-based SS are both performed by the designated NN algorithm used for VM conjecturing. Therefore, the key variables selected by NN-based SS will be more suitable for the said NN-based VM algorithm as far as conjecture accuracy is concerned. The etching process of semiconductor manufacturing is used as the illustrative example to test and verify the VM conjecture accuracy. One-hidden-layered back-propagation neural networks (BPNN-I) are adopted for establishing the NN models used in the NN-based SS method and the VMconjecture models. Test results show that the NN model created by the selected variables of NN-based SS can achieve better conjecture accuracy than that of MR-based SS. Simple recurrent neural networks (SRNN) are also tested and proved to be able to achieve similar results as those of BPNN-I.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Robotics and Automation, ICRA 2008
Pages3636-3641
Number of pages6
DOIs
Publication statusPublished - 2008 Sep 18
Event2008 IEEE International Conference on Robotics and Automation, ICRA 2008 - Pasadena, CA, United States
Duration: 2008 May 192008 May 23

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2008 IEEE International Conference on Robotics and Automation, ICRA 2008
CountryUnited States
CityPasadena, CA
Period08-05-1908-05-23

Fingerprint

Neural networks
Recurrent neural networks
Backpropagation
Etching
Semiconductor materials

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Lin, T. H., Cheng, F-T., Ye, A. J., Wu, W. M., & Hung, M. H. (2008). A novel key-variable sifting algorithm for virtual metrology. In 2008 IEEE International Conference on Robotics and Automation, ICRA 2008 (pp. 3636-3641). [4543768] (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ROBOT.2008.4543768
Lin, Tung Ho ; Cheng, Fan-Tien ; Ye, Aeo Juo ; Wu, Wei Ming ; Hung, Min Hsiung. / A novel key-variable sifting algorithm for virtual metrology. 2008 IEEE International Conference on Robotics and Automation, ICRA 2008. 2008. pp. 3636-3641 (Proceedings - IEEE International Conference on Robotics and Automation).
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title = "A novel key-variable sifting algorithm for virtual metrology",
abstract = "This work proposes an advanced key-variable selecting method, the neural-network-based stepwise selection (NN-based SS) method, which can enhance the conjecture accuracy of the NN-based virtual metrology (VM) algorithms. Multi-regression-based (MR-based) SS method is widely applied in dealing with key-variable selecting problems despite that it may not guarantee finding the best model based on its selected variables. However, the variables selected by MR-based SS may be adopted as the initial set of variables for the proposed NN-based SS to reduce the SS process time. The backward elimination and forward selection procedures of the proposed NN-based SS are both performed by the designated NN algorithm used for VM conjecturing. Therefore, the key variables selected by NN-based SS will be more suitable for the said NN-based VM algorithm as far as conjecture accuracy is concerned. The etching process of semiconductor manufacturing is used as the illustrative example to test and verify the VM conjecture accuracy. One-hidden-layered back-propagation neural networks (BPNN-I) are adopted for establishing the NN models used in the NN-based SS method and the VMconjecture models. Test results show that the NN model created by the selected variables of NN-based SS can achieve better conjecture accuracy than that of MR-based SS. Simple recurrent neural networks (SRNN) are also tested and proved to be able to achieve similar results as those of BPNN-I.",
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Lin, TH, Cheng, F-T, Ye, AJ, Wu, WM & Hung, MH 2008, A novel key-variable sifting algorithm for virtual metrology. in 2008 IEEE International Conference on Robotics and Automation, ICRA 2008., 4543768, Proceedings - IEEE International Conference on Robotics and Automation, pp. 3636-3641, 2008 IEEE International Conference on Robotics and Automation, ICRA 2008, Pasadena, CA, United States, 08-05-19. https://doi.org/10.1109/ROBOT.2008.4543768

A novel key-variable sifting algorithm for virtual metrology. / Lin, Tung Ho; Cheng, Fan-Tien; Ye, Aeo Juo; Wu, Wei Ming; Hung, Min Hsiung.

2008 IEEE International Conference on Robotics and Automation, ICRA 2008. 2008. p. 3636-3641 4543768 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Cheng, Fan-Tien

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AU - Hung, Min Hsiung

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N2 - This work proposes an advanced key-variable selecting method, the neural-network-based stepwise selection (NN-based SS) method, which can enhance the conjecture accuracy of the NN-based virtual metrology (VM) algorithms. Multi-regression-based (MR-based) SS method is widely applied in dealing with key-variable selecting problems despite that it may not guarantee finding the best model based on its selected variables. However, the variables selected by MR-based SS may be adopted as the initial set of variables for the proposed NN-based SS to reduce the SS process time. The backward elimination and forward selection procedures of the proposed NN-based SS are both performed by the designated NN algorithm used for VM conjecturing. Therefore, the key variables selected by NN-based SS will be more suitable for the said NN-based VM algorithm as far as conjecture accuracy is concerned. The etching process of semiconductor manufacturing is used as the illustrative example to test and verify the VM conjecture accuracy. One-hidden-layered back-propagation neural networks (BPNN-I) are adopted for establishing the NN models used in the NN-based SS method and the VMconjecture models. Test results show that the NN model created by the selected variables of NN-based SS can achieve better conjecture accuracy than that of MR-based SS. Simple recurrent neural networks (SRNN) are also tested and proved to be able to achieve similar results as those of BPNN-I.

AB - This work proposes an advanced key-variable selecting method, the neural-network-based stepwise selection (NN-based SS) method, which can enhance the conjecture accuracy of the NN-based virtual metrology (VM) algorithms. Multi-regression-based (MR-based) SS method is widely applied in dealing with key-variable selecting problems despite that it may not guarantee finding the best model based on its selected variables. However, the variables selected by MR-based SS may be adopted as the initial set of variables for the proposed NN-based SS to reduce the SS process time. The backward elimination and forward selection procedures of the proposed NN-based SS are both performed by the designated NN algorithm used for VM conjecturing. Therefore, the key variables selected by NN-based SS will be more suitable for the said NN-based VM algorithm as far as conjecture accuracy is concerned. The etching process of semiconductor manufacturing is used as the illustrative example to test and verify the VM conjecture accuracy. One-hidden-layered back-propagation neural networks (BPNN-I) are adopted for establishing the NN models used in the NN-based SS method and the VMconjecture models. Test results show that the NN model created by the selected variables of NN-based SS can achieve better conjecture accuracy than that of MR-based SS. Simple recurrent neural networks (SRNN) are also tested and proved to be able to achieve similar results as those of BPNN-I.

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U2 - 10.1109/ROBOT.2008.4543768

DO - 10.1109/ROBOT.2008.4543768

M3 - Conference contribution

SN - 9781424416479

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 3636

EP - 3641

BT - 2008 IEEE International Conference on Robotics and Automation, ICRA 2008

ER -

Lin TH, Cheng F-T, Ye AJ, Wu WM, Hung MH. A novel key-variable sifting algorithm for virtual metrology. In 2008 IEEE International Conference on Robotics and Automation, ICRA 2008. 2008. p. 3636-3641. 4543768. (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ROBOT.2008.4543768