Control-Oriented Modelling of an Experimental Ar-O2 Plasma Process

Development of a control-oriented model of an experimental plasma reactor is presented in this paper. The model structure is suitably partitioned in order to facilitate any subsequent control design. In the model, the linear dynamical part is conveniently separated from a static nonlinearity, which in turn allows identification to be performed for both parts independently. Validation results indicate that the model gives a reasonable representation of the studied plasma process

Control problem classification for a plasma process

The use of first-principles based models of plasma processes is discussed in this paper. Such processes are essentially highly nonlinear, feature complex interactions and are difficult to analyse. As an illustration of the nature of firstprinciple based models, a characterisation of a simple plasma process is presented in this work. Quantification of the nonlinearity, in terms of steady-state and dynamics behavior, is carried out for the studied process. The use of Hammerstein model and its applicability to the plasma process is also investigated. A basic stabilising controller for the simple plasma process is designed and its performance is analysed

Nonlinear Control Design for a Plasma Process

This paper presents a nonlinear control design for a first-principles based model of an argon plasma process. In this study, a Hammerstein-type structure was employed as a basis for a feedback control design. Artificial neural networks were used to accurately model the static nonlinearity. In the developed Hammerstein model, variations in the process dynamics were accounted for by considering parametric uncertainty. A control design strategy based on μ-synthesis was applied to deliver good tracking performance and disturbance rejection

Impedance matching controller for an inductively coupled plasma chamber: L-type Matching Network Automatic Controller

Plasma processing is used in a variety of industrial systems, including semiconductor manufacture (deposition and etching) and accurate control of the impedance matching network is vital if repeatable quality is to be achieved at the manufacturing process output. Typically, impedance matching networks employ series (tune) and parallel (load) capacitors to drive the reflection coefficient on the load side of the network to zero. The reflection coefficient is normally represented by real and imaginary parts, giving two variables to be controlled using the load and tune capacitors. The resulting problem is therefore a nonlinear, multivariable control problem. Current industrial impedance matching units employ simple single-loop proportional controllers, which take no account of interaction between individual channels and, in many cases, may fail to tune altogether, if the starting point is far away from the matching point. A hierarchical feedback controller is developed which, at the upper level, performs a single-loop tuning, but with the important addition of a variable sign feedback gain. When convergence to a region in the neighbourhood of the matching point is achieved, a dual single-loop controller takes over, which gives fine tuning of the matching network

Control problem classification for a plasma process

The use of first-principles based models of plasma processes is discussed in this paper. Such processes are essentially highly nonlinear, feature complex interactions and are difficult to analyse. As an illustration of the nature of firstprinciple based models, a characterisation of a simple plasma process is presented in this work. Quantification of the nonlinearity, in terms of steady-state and dynamics behavior, is carried out for the studied process. The use of Hammerstein model and its applicability to the plasma process is also investigated. A basic stabilising controller for the simple plasma process is designed and its performance is analysed

On the modelling and closed loop control of an inductively coupled plasma chamber

As a first step towards real time, multivariable control of an argon/ oxygen plasma, the implementation of real time control of ion flux in an inductively coupled argon plasma through modulation of the RF power is described. It is demonstrated that an elementary PID controller does not guarantee stable control of ion flux over a range of operating points and hence that more elaborate control strategies must be considered. The design and testing of control algorithms is facilitated by suitable dynamical models of a process. A model of the inductively coupled plasma chamber which is suitable for control simulations is described. Ongoing and future work are discussed

Control-Oriented Modelling of an Experimental Ar-O2 Plasma Process

Development of a control-oriented model of an experimental plasma reactor is presented in this paper. The model structure is suitably partitioned in order to facilitate any subsequent control design. In the model, the linear dynamical part is conveniently separated from a static nonlinearity, which in turn allows identification to be performed for both parts independently. Validation results indicate that the model gives a reasonable representation of the studied plasma process

Control-Oriented Modelling of an Experimental Ar-O2 Plasma Process

Development of a control-oriented model of an experimental plasma reactor is presented in this paper. The model structure is suitably partitioned in order to facilitate any subsequent control design. In the model, the linear dynamical part is conveniently separated from a static nonlinearity, which in turn allows identification to be performed for both parts independently. Validation results indicate that the model gives a reasonable representation of the studied plasma process

Control-Oriented Modelling of an Experimental Ar-O2 Plasma Process

Development of a control-oriented model of an experimental plasma reactor is presented in this paper. The model structure is suitably partitioned in order to facilitate any subsequent control design. In the model, the linear dynamical part is conveniently separated from a static nonlinearity, which in turn allows identification to be performed for both parts independently. Validation results indicate that the model gives a reasonable representation of the studied plasma process