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.
Recent works have shown that an exponentially weighted moving average (EWMA) controller can be used on semiconductor processes to maintain process targets over extended periods for improved product quality and decreased machine downtime. Proper choice of controller parameters (EWMA weights) is critical to the performance of this system. This work examines how different process factors affect the optimal controller parameters. We show that a function mapping from the disturbance state (magnitude of linear drift and random noise) of a given process to the corresponding optimal EWMA weights can be generated, and an artificial neural network (ANN) trained to learn the mapping. A self-tuning EWMA controller is proposed which dynamically updates its controller parameters by estimating the disturbance state and using the ANN function mapping to provide updates to the controller parameters. The result is an adaptive controller which eliminates the need for an experienced engineer to tune the controller, thereby allowing it to be more easily applied to semiconductor processes.
In previous work, we have formalized the notions of "planarization length" and "planarization response function" as key parameters that characterize a given CMP consumable set and process. Once extracted through experiments using carefully designed characterization mask sets, these parameters can be used to predict polish performance in CMP for arbitrary product layouts. The methodology has proven effective at predicting oxide interlevel dielectric planarization results.In this work, we discuss extensions of layout pattern dependent CMP modeling. These improvements include integrated up and down area polish modeling; this is needed to account for both density dependent effects, and step height limits or step height perturbations on the density model. Second, we discuss applications of the model to process optimization, process control (e.g. feedback compensation of equipment drifts), and shallow trench isolation (STI) polish. Third, we propose a framework for the modeling of pattern dependent effects in copper CMP. The framework includes "removal rate diagrams" which concisely capture dishing height and step height dependencies in dual material polish processes. I. MOTIVATION: PATTERN DEPENDENT CMP CONCERNSThe motivation for this work is the presence of substantial pattern dependencies in CMP. As illustrated in Fig. 1, these concerns arise in a variety of key CMP process applications. In oxide or interlevel dielectric (ILD) CMP, the global planarity or oxide thickness differences in different regions across the chip is a key concern. In addition, the remaining local step height (or height differences in the oxide over patterned features and between patterned features) may also be of concern, although such local step heights are typically small compared to the global nonplanarity across the chip resulting from pattern density dependent planarization. In shallow trench isolation (STI), one is typically concerned about dishing within oxide features resulting from over-polish, as well as the erosion of supporting nitride and in some cases the details of the corner rounding near active areas. In metal polishing (such as in copper damascene), one is concerned also with dishing into metal lines, as well as the erosion of supporting oxide or dielectric spaces in arrays between lines.In this paper, we begin by reviewing previous work on characterization and modeling of oxide CMP pattern dependencies. In Section II, we review the density-dependent oxide CMP model, as well as the important determination of "effective density" based upon a planarization length or planarization response function determination. In Section III, we also review a recent advance in oxide modeling, through which a step height dependent model (proposed elsewhere) has been integrated with the effective density model to produce an integrated time-evolution model for improved accuracy in step height and down area polish prediction. In Section IV we present example applications of the oxide characterization and modeling methodology. These inclu...
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