Real time plasma etch process modeling by neural networks

Jennie Si, Yuan Ling Tseng, Mike Clayton, Steve Felker, Bob Yoo, Jim Martinez, Jim Durham, Kim Dang

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

1 Scopus citations

Abstract

In the present paper we address the problem of control relevant process modeling from production data for the N-Well Reactive Ion Etching processed by LAM Rainbow Etchers. Due to physical constraints we consider building an empirical neural network model using one lot of data which usually contains 24 wafers. Using the existence result of feedforward networks as universal approximators, we experimentally developed different network structures as models of the etching process under investigation. Our results are built upon extensive simulations on different lots of the process. The same modeling idea is also extended to use the network model to predict the end point detection signal prior the process of one wafer.

Original languageEnglish (US)
Title of host publicationIEEE Symposium on Emerging Technologies & Factory Automation, ETFA
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages347-352
Number of pages6
StatePublished - 1997
EventProceedings of the 1997 IEEE 6th International Conference on Emerging Technologies and Factory Automation, ETFA'97 - Los Angeles, CA, USA
Duration: Sep 9 1997Sep 12 1997

Other

OtherProceedings of the 1997 IEEE 6th International Conference on Emerging Technologies and Factory Automation, ETFA'97
CityLos Angeles, CA, USA
Period9/9/979/12/97

ASJC Scopus subject areas

  • Engineering(all)

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    Si, J., Tseng, Y. L., Clayton, M., Felker, S., Yoo, B., Martinez, J., Durham, J., & Dang, K. (1997). Real time plasma etch process modeling by neural networks. In IEEE Symposium on Emerging Technologies & Factory Automation, ETFA (pp. 347-352). IEEE.