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The oxide etch rate of a single chamber of plasma etch tool is estimated from plasma impedance data collected during the etch process. The etch rate is estimated using a linear statistical model and etch rate measurements performed on special test wafers. Stepwise regression is used to select possible predictors from a large pool of summary statistics calculated from the plasma impedance waveforms. The relationship of the estimated mean etch rate to yield and potential yield optimization is explored. An example application of an advanced process controller to optimize the yield of the wafers processed by the etch tool in the presence of varying chamber conditions is also presented.

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We propose a mefhod for process moniforing of a semiconductor manufacrrrriiig process. Independenf Component Analysis (ICA) is applied to characterize Etesf parameter data. We calculate angular confidence intervals for the model, eliminate marginally sign8cant components, and implement confrol cham for sign8canf componenfs of inreresf. Alarms are generated off of deviationr in the charted componenfs. Alarms are easily used in process diagnosis based on the interpretation of the independent components.

No abstract