Abstract:This paper presents a learning approach for wafer temperature control in a rapid thermal processing system (RTP). RTP is very important for semiconductor processing system and requires an accurate trajectory following. Numerous studies have addressed this problem and most research on this problem requires exact knowledge of the system dynamics. The various approaches do not guarantee the desired performance in practical applications when there exist some modeling errors between the model and the actual system.… Show more
The sections in this article are
Introduction
Artificial Intelligence Tools
Process Modeling
Optimization
Process Monitoring and Control
Process Diagnosis
Yield Modeling
Conclusion
The sections in this article are
Introduction
Artificial Intelligence Tools
Process Modeling
Optimization
Process Monitoring and Control
Process Diagnosis
Yield Modeling
Conclusion
For a class of repetitive linear discrete time-invariant systems with higher relative degree, a higher-order gain-adaptive iterative learning control (HOGAILC) is developed while minimizing the energy increment of two adjacent tracking errors with the argument being the iteration-time-variable learning-gain vector (ITVLGV). By taking advantage of rows/columns exchanging transformation of matrix, the ITVLGV is achieved in an explicit form which is dependent upon the system Markov parameters and adaptive to the iterationwise tracking-error vector. Algebraic derivation demonstrates that the HOGAILC is strictly monotonously convergent. On the basis of the adaptive mode, a damping quasi-HOGAILC strategy is exploited while the uncertainties of the system Markov parameters exist. Rigorous analysis delivers that the damping quasi-scheme is strictly monotonically convergent and thus the HOGAILC mechanism is robust to a wider range of uncertainty of system parameters and the damping factor may relax the uncertainty range. Numerical simulations are made to illustrate the validity and the effectiveness.
K E Y W O R D Sdamping factor, gain-adaptive iterative learning control, higher relative degree, iteration-time-variable learning-gain vector, strictly monotonic convergence, uncertainty 1 3960
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.