Abstract
Long-range predictive control of a quartz chamber furnace used in microelectronic technology is discussed. It is based on a technique called rapid thermal processing (short thermal cycles, reaching high temperatures for a short time). The furnace temperature and the electric power supplied to the furnace are selected as controlled and control variables, respectively. The control algorithm is based on the minimization of a quadratic criterion involving the input and output traektng errors- it is derived in the receding-horizon sense. To represent the complex dynamics of the furnace, a single input/output controlled autoregressive integrating moving average model (CARIMA) was adopted. The model parameters are estimated on-line using a robust identification scheme which includes data normalization, covariance matrix factorization, adjustable forgetting factor, etc. Experimental results are included which demonstrate the capability of the adaptive control algorithm considered with rapid thermal processes.
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