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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL 4, NO I, FEBRUARY 1991 Use of Influence Diagrams and Neural Networks in
 

Summary: I I I I
52
IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL 4, NO I, FEBRUARY 1991
Use of Influence Diagrams and Neural Networks in
Modeling Semiconductor Manufacturing Processes
Fariborz Nadi, Alice M. Agogino, and David A. Hodges, Fellow, IEEE
Abstract-An adaptive learning architecture has been developed for
modeling manufacturing processes involving several controlling vari-
ables. This paper describes the application of the new architecture to
process modeling and recipe synthesis for deposition rate, stress, and
film thickness in low pressure chemical vapor deposition (LPCVD) of
undoped polysilicon. In this architectqre the model for a process is
generated by combining the qualitative knowledge of human experts,
tqptured in the form of influence diagrams, and the learning abilities
of neural networks for extracting the quantitative knowledge that re-
lates parameters of a process. To evaluate the merits of this method-
ptogy, we have compared the accuracy of these new models to that of
more conventionalmodels generated by the use of first principles and/
or statistical regression analysis. Accuracy of the different models is
compared using the same empirical data sets form realistic experi-

  

Source: Agogino, Alice M. - Department of Mechanical Engineering, University of California at Berkeley

 

Collections: Engineering