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An integrated model is given for the components that make up a
rapid thermal processing (RTP) system. These components are the
lamp power dynamics, ray-tracing model, and the wafer thermal
dynamic model. The models for the components are integrated in
a numerical code to give a computer simulation of the complete
RTP system. The simulation can be used to investigate the
interaction of the furnace, lamp contour, and the control
system. Of the various techniques for controlling the
temperature of the RTP system,model-based control has the
greatest potential for attaining the best performance in the
field of conventional control, when the model is accurate. In
this thesis, some identification methods are introduced to help
obtain more accurate models from measured input-output data. At
first, identification techniques are presented to estimate the
parameters (time constant and gain) of step response. Then show
how to use the input-output measurements to obtain a black-box
model (autoregressive exogenous mo del) of the RTP system, which
turns out to have better prediction capability. The identified
models are useful not only to control law design but also to
analysis of system characteristics.We apply the tracking system
technique that is known in the digital control literature for
the model-based control, and propose a controller-switching rule
throughout the nonlinear operating region. To overcome ill
performance of the tracking system due to the inaccuracy of the
identified model, the neural fuzzy network for inverse control
is proposed. The key advantage of neural fuzzy approach over
traditional ones lies on that the approach doesn't require a
mathematical description of the system while controlling. The
neural fuzzy network that we use is the Self-cOnstructing
Neural Fuzzy Inference Network (SONFIN) proposed by Junag and
Lin. Temperature control involves maintaining a spatially
uniform temperature distribution across the wafer while
tracking a temp orally varying temperature trajectory such as a
rapid ramp to processing temperatures. The combination of lamp
added RTP system and the MIMO SONFIN is shown to be able to
track a temporally varying temperature trajectory and maintain
the uniformity of the spatial temperature distribution
simultaneously.
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