A survey of run-to-run control for batch processes
Kai Liu1, YangQuan Chen2, Tao Zhang3
1School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, 100083, China; Lam Research Corporation, 4300 Cushing Pkwy, Fremont, CA 94538, USA; School of Engineering, University of California, Merced, 5200 Lake Rd, Merced, CA 95340, USA.
ISA Transactions
|October 2, 2018
Summary
This review examines run-to-run (R2R) control methods for batch processes, analyzing controllers like EWMA, d-EWMA, MPC, OAQC, and ANN. It compares their adaptability and robustness through simulations and case studies.
Area of Science:
- Process Control
- Manufacturing Engineering
- Data Science
Background:
- Run-to-run (R2R) control is crucial for minimizing variability in semiconductor manufacturing.
- R2R controllers adjust processes after each batch to maintain stability and quality.
Purpose of the Study:
- To provide a comprehensive literature review of R2R control methods for batch processes.
- To analyze and compare the principles, robustness, and adaptability of various R2R controllers.
Main Methods:
- Literature review of major R2R control techniques.
- Analysis of Exponentially Weighted Moving Average (EWMA), Double EWMA (d-EWMA), Model Predictive Control (MPC), Optimizing Adaptive Quality Controller (OAQC), and Artificial Neural Network (ANN).
- Simulation examples and case studies including Chemical Mechanical Planarization (CMP), furnace process control (MIMO), and blood glucose (BG) management.
Main Results:
- Detailed principles and analyses of key R2R controllers are presented.
- Simulation results demonstrate the comparative robustness and adaptability of different R2R methods.
- Case studies illustrate practical applications in diverse industrial and biomedical settings.
Conclusions:
- The paper offers recommendations for future research directions in R2R control.
- Highlights the importance of selecting appropriate R2R controllers based on process characteristics and objectives.
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