Abstract
Several Kalman filter designs were developed and tested for controlling a spray etching process. A hybrid extended Kalman filter was found to yield biased estimates in the presence of process-model mismatch. This issue was resolved by augmenting the state vector with poorly known parameters and developing a proportional-integral Kalman filter. These parameters are treated as stochastic processes, through which the estimate bias of the extended Kalman filter is eliminated. The resulting filter designs are capable of providing accurate estimates of the etching species concentrations as well as making available frequent estimates of the etch profile characteristics, which are only measured at infrequent intervals.
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