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Proceedings Paper

Etch process characterization using neural network methodology: a case study
Author(s): Michael T. Mocella; James A. Bondur; Terry R. Turner
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Paper Abstract

Polysilicon etching in a single-wafer, parallel-plate, magnetically- enhanced RIE tool has been examined using two different approaches to the non-physical modeling of the system characteristics. The behavior of both process responses (polysilicon and oxide etch rates) and plasma parameters (voltage and current metrics) have been examined as a function of five variables (rf power, pressure, magnetic field, gas flow rate, and He backside cooling). The variable-response mapping was examined using both neural network and response surface approaches. The greater fitting power of the former method is demonstrated in a side-by-side, internally consistent comparison of the same data set using these two approaches.

Paper Details

Date Published: 1 January 1992
PDF: 11 pages
Proc. SPIE 1594, Process Module Metrology, Control and Clustering, (1 January 1992); doi: 10.1117/12.56637
Show Author Affiliations
Michael T. Mocella, DuPont Electronics (United States)
James A. Bondur, Applied Materials, Inc. (United States)
Terry R. Turner, SEMATECH (United States)

Published in SPIE Proceedings Vol. 1594:
Process Module Metrology, Control and Clustering
Cecil J. Davis; Irving P. Herman; Terry R. Turner, Editor(s)

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