Integrate on-line experiment and the control methods for changes in a dynamic model
| Autor: | Chih-Hung Jen, 任志宏 |
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| Rok vydání: | 2007 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 95 During the past decade, a variety of ‘run-to-run’ (R2R) control schemes have been proposed and investigated extensively under various semiconductor manufacturing methods. However, such control has a problem when it is suddenly faced with a larger process change that does not satisfy the control requirement. In view of this, a new process control framework is proposed that integrates response surface modeling, on-line experiment, and R2R control ideas. The primary objective of this study is to improve dynamic model parameter prediction, enabling more effective optimized recipe calculation. The recursive least squares (RLS) algorithm is used to evaluate changes in process parameters to establish the degree of control for the next period. If the evaluated parameter values exceed a joint parameter threshold, the recipe moves to a new optimum point. This moment, which will apply the design of experiment concept continuously to collect process data in the experimental range, uses this data with the least squares error (LSE) method to estimate the new model parameters. The proposed control strategy uses the minimized total cost principle (the cost function form includes an expected off-target and controllable factors adjustment) and applies the Broyden-Fletch-Goldfarb-Shanno (BFGS) algorithm to obtain a recipe for the next period. Simulation studies show that the proposed system has better control performance than the traditional self-tuning controller. When the model is provided with a slight variation, operating and monitoring are easier than using evolutionary operation (EVOP). In the relevant chemical mechanical planarization (CMP) and polysilicon gate etching applications in semiconductor manufacturing, two critical chip fabrication steps are used to illustrate the proposed control system in a dynamic process. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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