Developing a selection scheme for dual virtual-metrology outputs

Wei Ming Wu, Fan Tien Cheng, Deng Lin Zeng, Tung Ho Lin, Jyun Fang Chen

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

5 Citations (Scopus)

Abstract

This paper proposes a selection scheme (S-scheme) between neural-network (NN) and multiple-regression (MR) outputs of a virtual metrology system (VMS). Both NN and MR are applicable algorithms for implementing VM conjecture models. But a MR algorithm may achieve better accuracy only with a stable process, whereas a NN algorithm may has superior accuracy when equipment property drift or shift occurs. To take advantage of the merits of both MR and NN algorithms, the S-scheme is proposed to enhance virtual-metrology (VM) conjecture accuracy. Two illustrative examples in the CVD process of fifth generation TFT-LCD are used to test and compare the conjecture accuracy among solo NN, solo MR, and S-scheme. One-hidden-layered back-propagation neural network (BPNN-I) is adopted for establishing the NN conjecture model. Test results show that the conjecture accuracy of S-scheme can achieve superior accuracy than solo NN and solo MR algorithms.

Original languageEnglish
Title of host publication4th IEEE Conference on Automation Science and Engineering, CASE 2008
Pages230-235
Number of pages6
DOIs
Publication statusPublished - 2008 Nov 3
Event4th IEEE Conference on Automation Science and Engineering, CASE 2008 - Washington, DC, United States
Duration: 2008 Aug 232008 Aug 26

Publication series

Name4th IEEE Conference on Automation Science and Engineering, CASE 2008

Other

Other4th IEEE Conference on Automation Science and Engineering, CASE 2008
CountryUnited States
CityWashington, DC
Period08-08-2308-08-26

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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