Optimal etch time control design using neuro-dynamic programming

L. Yang, Jennie Si

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

Abstract

This paper focuses on using a new learning algorithm, namely neural dynamic programming (NDP), to design the optimal etch time control system for a reactive ion etch process. First a predictive neural network model is built. This model represents the relation between some state variables and the resulting thickness remain. The NDP is employed to determine the optimal etch time based on the predictive film thickness remain model. Simulation results show that NDP is a viable learning optimization tool. The controlled film thickness remains have smaller variances in a few tested lots of wafers than those measured from 89 wafers during production.

Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Semiconductor Manufacturing Conference, Proceedings
Pages75-78
Number of pages4
StatePublished - 2001
EventIEEE International Symposium on Semiconductor Manufacturing (ISSIM) 2001 - San Jose, CA, United States
Duration: Oct 8 2001Oct 10 2001

Other

OtherIEEE International Symposium on Semiconductor Manufacturing (ISSIM) 2001
CountryUnited States
CitySan Jose, CA
Period10/8/0110/10/01

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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