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Project Progress Overview:Process Control, Modeling and Diagnosis

S. Joe Qin

Department of Chemical EngineeringThe University of Texas at Austin

Austin, Texas 78712512-471-4417

qin@che.utexas.edu Control.che.utexas.edu/qinlab

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Current Projects

• Chemical process data based monitoring and control

NSF, DuPont

• Microelectronics process monitoring and control

NSF, AMD

• Process Fault Detection and Identification

Texas ARP, DuPont, Union Carbide

• Control Performance Monitoring – WeyCo

• Subspace Identification

• DAE/MPC

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Process Monitoring

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Data Analysis: clusters

308 variables

Process variables

Setpoints

Output variables

Monitoring variables

Process and monitoring variables were used in this analysis, 103 variables.

Four clusters

Red cluster: normal

Blue, green, and black clusters: faulty.

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Process data is divided in 7 blocks.

Faulty block is located applying decentralized monitoring.

Block 2 is where the main fault is located.

800 1000 1200 1400

1

2

3

Bloc

k # 1

Squared Prediction Error

800 1000 1200 1400

2040

60

Bloc

k # 2

Squared Prediction Error

800 1000 1200 1400

2468

10

Bloc

k # 3

800 1000 1200 1400

1

2

3

Bloc

k # 4

800 1000 1200 1400

1

2

3

Bloc

k # 5

800 1000 1200 1400

2

4

Bloc

k # 6

samples

800 1000 1200 1400

12

3Bl

ock #

7

samples

Hierarchical Monitoring

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Using the SPE index the faulty blocks are again block 2 and block 3.

The variables contributing to the out-of-control situation in block 2 are mainly variables 16 and 19.

In block 3 mainly variables 25 and 28 are responsible for the out-of-control situation.

Decentralized monitoring approach gives much clearer indication of the faulty variables.

1 2 3 4 5 6 7

2468

101214

Block

Samp

le # 1

238

Block contributions

0 2 4 6 8 10 12 14 16 18 20

5

10

15

Samp

le # 1

238

Variables number

Variable contribution to SPE in block 2

10 12 14 16 18 20 22 24 26 28 300

2

4

Samp

le # 1

238

Variables number

Variable contribution to SPE in block 3

Contributions in Faulty Blocks

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Block SPE index for all blocks along samples of data.

Any significant departure from the horizontal plane is an indication of a fault.

050

100150

200250

0

2

4

6

80

10

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samplesBlock

Bloc

k SPE

inde

x

Block SPE Index

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Proposed Future Work

Process monitoring identifies control problems; control performance assessment is interfered by process disturbances

Process monitoring vs. control performance

Interrelated

Elements in a controlled process— Process, disturbances, faults— Controllers— Actuators— Sensors— Operators

All these are to be isolated with the use of data or a model, requiring an integrated approach

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Microelectronics

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Effects of Open Area

Small open area leads to noisy signal and hard-to-detect endpoint

0 20 40 60 80 100930

940

950

960

970

980

990

1000

1010Large open area

time (sec)

Inte

nsity

0 50 100 150540

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610

time (sec)

Small open area

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Final Endpoint Signal

0 20 40 60 80 100 120-50

0

50

100A Final Endpoint Signal

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Verification of the Detected Endpoint

Endpoint detection is verified by designed experiments

SEM pictures also show that vias are cleared

The thickness correlates well with the etch time using the endpoint detection algorithm

Endpoint Time vs. Thickness(For Different Open Areas)

R2 = 0.9906

R2 = 0.9725

R2 = 0.991255

60

65

70

75

80

85

90

95

100

5000 6000 7000 8000 9000 10000 11000 12000 13000

Thickness (Å)

EP

Tim

e (s

ec)

0.47%

0.71%

1.05%

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Subspace Identification

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Subspace Identification

• Represents a new class of methods that produce a general state space model from process data

• The modeling procedure is almost automatic after the data are collected

• Most algorithms work for a wide variety of noise models, including errors-in-variables (EIV) models

• Deals with input or output collinearity for coupled inputs or parallel outputs

• Optimal/Kalman filtering or estimation is a natural by-product

• Extensions to nonlinear processes are available in specific model forms

• Model adaptation is easy to implement

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Problem Formulation

Assume the process is represented by:

where

Subspace ID:

Given input output sequences{u(k)}, {y(k)}, extract the system matrices A, B, C and D.

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Summary of Results

• PCA, Dynamic PCA, and Subspace Id. (Li and Qin, 2000)

DPCA by including lagged variables is not consistent

SMI based IDPCA is consistent for linear dynamics

• SMI model for fault detection (Qin and Li, 2000)

Need only the observability and Toeplitz matrices

Maximized sensitivity

• Errors-in-variables SMI using PCA (Wang and Qin, 2000)

PCA with instrumental variables provides consistent state space models for errors-in-variables formulation

SMI vs. Kalman Filter vs. PCA vs. CVA vs. PLS???

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Publications

1.        Li, W. and S.J. Qin (2000). Consistent dynamic PCA based on errors-in-variables subspace identification. Accepted by J. of Process Control, July, 2000.

2.        Qin, S.J., S. Valle and M. Piovoso (2000). On unifying multi-block analysis with application to decentralized process monitoring. Submitted to J. Chemometrics.

3.        Qin, S. J. and W. Li (2000). Detection and identification of faulty sensors in dynamic processes with maximized sensitivity, Submitted to AIChE Journal.

4.        Yue, H. and S.J. Qin (2000). Reconstruction based fault identification using a combined index. Submitted to I&EC Research.

5.        Yue, H., S.J. Qin, J. Wiseman, and A. Toprac (2000). Plasma etching endpoint detection using multiple wavelengths for small open-area wafers. Submitted to J. of Vacuum Science & Technology.

6.     Misra, M., S.J. Qin, H. Yue and C. Ling (2000). Multivariate process monitoring and fault identification using multi-scale PCA, Submitted to Comput. Chem. Engng.

7.        Misra, M., Kumar, S., Qin, S.J., and Seemann, D. (2000). Error based criterion for on-line wavelet data compression. Accepted by J. of Process Control.

8.        Li, W., H. Yue, S. Valle-Cervantes, and Qin, S.J. (2000). Recursive PCA for adaptive process monitoring. J. of Process Control, 10, 471 -- 486.

9.        H. Yue, S.J. Qin, R. Markle, C. Nauert, and M. Gatto (2000). Fault detection of plasma etchers using optical emission spectra. IEEE Trans. on Semiconductor Manufacturing, 13, 374-385.

10.     Misra, M., Kumar, S., Qin, S.J., and Seemann, D. (2000). Recursive on-line data compression and error analysis using wavelet technology. AIChE Journal, 46, 119-132.

11. Qin, S.J and R. Dunia (2000). Determining the number of principal components for best reconstruction. J. of Process Control, 10, 245-250.

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Acknowledging Collaborators

Modeling/SMI

D. Di Ruscio, W. Larimore

Diagnosis

M. Piovoso, T. Ogunnaike, A. Toprac, C. Ling, J. Guiver

Pulp and Paper

T. Swanda, J. Watkins

Microelectronics

A. Toprac, R. Markle, H. Yue, M. Misra

Current Students

S. Valle, C. McNabb, H. Potrykus, J. Wang, R. Mak

R. Dunia (post doc associate)