Control relevant RIE modeling by neural networks from real time production state sensor measurements

Jennie Si, Yuan Ling Tseng

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.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherIEEE
Pages1583-1587
Number of pages5
Volume3
StatePublished - 1997
EventProceedings of the 1997 American Control Conference. Part 3 (of 6) - Albuquerque, NM, USA
Duration: Jun 4 1997Jun 6 1997

Other

OtherProceedings of the 1997 American Control Conference. Part 3 (of 6)
CityAlbuquerque, NM, USA
Period6/4/976/6/97

ASJC Scopus subject areas

  • Control and Systems Engineering

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