Project Progress Overview: Process Control, Modeling and Diagnosis

1. Project Progress Overview: Process Control, Modeling and Diagnosis S. Joe Qin Department of Chemical Engineering The University of Texas at Austin Austin, Texas 78712 512-471-4417 qin@che.utexas.edu Control.che.utexas.edu/qinlab

2. 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

3. Process Monitoring

4. 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. Data Analysis: clusters

5. Process data is divided in 7 blocks. Faulty block is located applying decentralized monitoring. Block 2 is where the main fault is located. Hierarchical Monitoring

6. 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. Contributions in Faulty Blocks

7. Block SPE index for all blocks along samples of data. Any significant departure from the horizontal plane is an indication of a fault. Block SPE Index

8. 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

9. Microelectronics

10. Effects of Open Area • Small open area leads to noisy signal and hard-to-detect endpoint

11. Final Endpoint Signal

12. 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

13. Subspace Identification

14. 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

15. 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.

16. 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???

17. 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.

18. 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)