NN-based key-variable selection method for enhancing virtual metrology accuracy

Tung Ho Lin, Fan Tien Cheng, Wei Ming Wu, Chi An Kao, Aeo Juo Ye, Fu Chien Chang

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

This paper proposes an advanced key-variable selection 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 selection problems despite the fact 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 merits of the NN-based SS method. 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 VM conjecture 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 and generalized regression neural network are also tested and proved to be able to achieve similar results as those of BPNN-I.

Original languageEnglish
Article number4773490
Pages (from-to)204-211
Number of pages8
JournalIEEE Transactions on Semiconductor Manufacturing
Volume22
Issue number1
DOIs
Publication statusPublished - 2009 Feb 1

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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