A prototype hardware/software system has been developed and applied to the control of single wafer chemicalmechanical polishing (CMP) processes. The control methodology consists of experimental design to build response surface and linearized control models of the process, and the use of feedback control to change recipe parameters (machine settings) on a lot by lot basis. Acceptable regression models for a single wafer polishing tool and process were constructed for average removal rate and nonuniformity which are calculated based on film thickness measurement at nine points on 8 in blanket oxide wafers. For control, an exponentially weighted moving average model adaptation strategy was used, coupled to multivariate recipe generation incorporating user weights on the inputs and outputs, bounds on the input ranges, and discrete quantization in the machine settings. We found that this strategy successfully compensated for substantial drift in the uncontrolled tool's removal rate. It was also found that the equipment model generated during the experimental design was surprisingly robust; the same model was effective across more than one CMP tool, and over a several month period. We believe that the same methodology is applicable to patterned oxide wafers; work is in progress to demonstrate patterned wafer control, to improve the control, communication, and diagnosis components of the system, and to integrate real-time information into the run by run control of the process.

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A multilevel hierarchical control system has been designed and is being applied to chemicalmechanical planarization ͑CMP͒ process control. The current implementation of the control system incorporates closed-loop run-to-run ͑R2R͒ control and open-loop real-time monitoring, and can accommodate inter-cell control. The R2R control element is enabled via a generic cell controller ͑GCC͒ implementation that provides flexible automated control of the process and equipment, multiple control algorithm branches and fuzzy logic decision capability among the branches, simulation capabilities, hardware and software independence, and extensive GUI support for control and data analysis. The R2R element utilizes a linear approximation multivariate control algorithm ͑branch͒ that supports individual exponential weighted moving average ͑EWMA͒ modeling of advices ͑outputs͒, weighting of inputs, granularity, and input bounding. The real-time element of the control system utilizes a partial least squares ͑PLS͒ algorithm to identify real-time equipment input trace patterns and relate these patterns to alarming conditions. The entire control system is designed to provide multivariate control of CMP process removal rate and uniformity. As a result of extensive design of experiments and testing, the R2R control level has been demonstrated to achieve good control of removal rate and fair control of uniformity. The addition of the real-time element is expected to improve process control and reduce R2R process noise, thus leading to a more effective R2R control element.

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