Share Email Print

Proceedings Paper

Model-based equipment diagnosis
Author(s): David J. Collins; Andrzej J. Strojwas; P. K. Mozumder
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A versatile methodology is described in which equipment models have been incorporated into a single process diagnostic system for the PECVD of silicon nitride. The diagnosis system has been developed and tested with data collected using an Applied Materials Precision 5000 single wafer reactor. The parametric equipment diagnosis system provides the basis for optimal control of multiple process responses by the classification of potential sources of equipment faults without the assistance of in-situ sensor data. The basis for the diagnosis system is the use of tuned empirical equipment models which have been developed using a physically-based experimental design. Nine individual site-specific models were used to provide an effective method of modeling the spatially-dependent process variations across the wafer with better sensitivity than mean-based models. The diagnostic system has been tested using data that was produced by adjusting the actual equipment controls to artificially simulate a variety of possible subtle equipment drifts and shifts. Statistical algorithms have been implemented which detect equipment drift, shift and variance stability faults using the difference between the predicted process responses to the off-line measured process responses. Fault classification algorithms have been developed to classify the most likely causes for the process drifts and shifts using a pattern recognition system based upon flexible discriminant analysis.

Paper Details

Date Published: 16 September 1994
PDF: 12 pages
Proc. SPIE 2336, Manufacturing Process Control for Microelectronic Devices and Circuits, (16 September 1994); doi: 10.1117/12.186776
Show Author Affiliations
David J. Collins, Carnegie Mellon Univ. (United States)
Andrzej J. Strojwas, Carnegie Mellon Univ. (United States)
P. K. Mozumder, Texas Instruments Inc. (United States)

Published in SPIE Proceedings Vol. 2336:
Manufacturing Process Control for Microelectronic Devices and Circuits
Anant G. Sabnis, Editor(s)

© SPIE. Terms of Use
Back to Top