Abbas Emami-Naeini

Modeling and control of distributed thermal systems

This paper investigates the application of model-based control design techniques to distributed temperature control systems. Multivariable controllers are an essential part of modern-day rapid thermal processing (RTP) systems. We consider all aspects of the control problem beginning with a physics-based model and concluding with implementation of a real-time embedded controller. The thermal system used as an example throughout is an RTP chamber widely used in semiconductor wafer processing. With its exceptionally stringent performance requirements (low nonuniformity of wafer temperature, high temperature ramp rates), RTP temperature control is a challenging distributed temperature control problem. Additionally, it is important in the semiconductor industry because of the progressively smaller thermal budget resulting from ever decreasing integrated circuit dimensions. Despite our emphasis on faster cold wall single-wafer processing RTP chambers, the approach described here for solving distributed temperature control problems is equally applicable to slower distributed thermal systems, such as hot-wall batch-processing furnaces. For the physical model, finite volume techniques are used to develop high-fidelity heat transfer models that may be used for both control design and optimal chamber design. Model-order reduction techniques are employed to reduce these models to lower orders for control system design. In particular, principal orthogonal decomposition techniques have been used to derive low order models. Techniques such as linear quadratic Gaussian H/sub 2//H/sub /spl infin// methods are employed for feedback control design. While the methods are illustrated here using a generic RTP system, they have been successfully implemented on commercial RTP chambers.

Run-to-run control of static systems

Run-to-run control using static linear models is examined. Conditions are presented for stability, (statistical) performance, and robustness using the standard control theory. A simulation example shows the usefulness of the method applied to a rapid thermal oxidation process.

Real-time model-based control system design and automation for gelcast drying process

Gelcasting is a new method for manufacturing advanced structural ceramics for use, for example, in the aerospace industry. The process involves a drying stage, where moisture, which constitutes approximately a quarter of the mass of the part, is removed in a commercial dryer. The control system design for the gelcast ceramic drying process is complicated by the requirement of minimizing the drying time while avoiding cracking during shrinkage of the part. The paper describes the application of Lyapunov theory for finding an exponentially stablizing controller for the nonlinear drying model. A RealSim/sup (T/)Rapid Prototyping model and the MATRIXx/sup (T)/ design automation environment are used for real time control, as well as feedback system implementation. It is shown that the proposed design and testing automation process with a user friendly interactive animation (IA) graphical user interface (GUI) provides an effective and efficient environment for real time control of the gelcast drying process.

Active Control of Flow Over a Backward-Facing Step

We describe a control-oriented model reduction process from partial differential equation (PDE) descriptions of aerodynamic flow systems, and the design of low-order feedback controllers using these procedures. An effective approach for flow model reduction for active control using balanced truncation approaches was developed. The techniques were applied to a flow control problem representative of aerospace problems: active control of flow over a modified 2D backward-facing step, which can be handled by most commercial finite element packages. Although the geometry is rather simple, the richness of the flow field is well documented. Feedback controllers were developed for disturbance rejection and tracking of the reattachment point for both high and low Reynolds numbers.