Statistical Process Control Based Supervisory Generalized Predictive Control of Thin Film Deposition Processes

[+] Author and Article Information
Jionghua Jin1

Department of Industrial and Operations Engineering, The University of Michigan, Ann Arbor, MI 48109-2117jhjin@umich.edu

Huairui Guo

Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721-0020

Shiyu Zhou

Deparment of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706


To whom correspondence should be addressed.

J. Manuf. Sci. Eng 128(1), 315-325 (Dec 15, 2004) (11 pages) doi:10.1115/1.2114912 History: Received September 05, 2003; Revised December 15, 2004

This paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed from SPC techniques, are used to monitor process changes and estimate the disturbance magnitudes during production. Based on the SPC monitoring results, different supervisory strategies are used to revise the disturbance models and the control law in the GPC to achieve a satisfactory control performance. A case study is provided to demonstrate the developed methodology.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 1

Chamber structure of thin film deposition process

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Figure 2

The structure of the supervisory GPC

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Figure 3

Initial production run data of step input and response

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Figure 4

AIC under different model structures

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Figure 5

Comparison of the modeling results with the real process response

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Figure 6

Impulse and step response of the thin film deposition process model

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Figure 7

A simulated control performance

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Figure 8

Mean-shift disturbance function and CUSUM monitoring control chart

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Figure 9

Comparison of control performance under mean-shift disturbances

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Figure 10

Comparison of model residual errors under mean shift

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Figure 11

CUSUM monitoring control chart for detecting a drift

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Figure 12

Control performance comparison under drift disturbances

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Figure 13

Comparison of model errors under linear drift

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Figure 14

Control performance comparison under the spike disturbance

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Figure 15

Control performance under a spike disturbance for a longer memory system



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