Conclusions

Non-threaded R2R control and virtual metrology are important components of process control systems, and both of them are designed to address the costly metrology operations. The threaded R2R controls do not share any information among different threads and the metrology data are diluted, so a larger metrology sampling rate is required to maintain the performance of the process control. How- ever, more metrology operations increase the cost of semiconductor manufacturing due to cycle time and metrology tool cost. In contrast, a non-threaded R2R control does not require a high sampling rate, because the metrology information among different control threads can be shared.

On the other hand, virtual metrology predicts the metrology data without conducting the actual measurements. The predicted metrology data can be used for either process monitoring or process control. The metrology related cost can be reduced by skipping on-line or off-line metrology operations. If the prediction quality is high enough, then this predicted metrology data can be fed into the R2R controllers. Process control and perhaps yield can be improved through variation reductions by VM and R2R controllers.

7.1.1 Hybrid Non-threaded Run-to-Run Control

The major problems associated with a non-threaded R2R controller include unobservable control systems, the continuous change of model dimensions and the high computational cost of state estimation. We addressed the ”unobservable control system” problem by a novel hybrid non-threaded R2R controller design, where the controller mode can be downgraded from the non-threaded mode to

the threaded control automatically, when the unobservable problems occur. The changing model dimension issue was solved by reserving dummy contexts in the non-threaded R2R controller without adding any other complexity. After the non- threaded R2R controller had been deployed in the real production environment, we encountered the same high computational costs of the state estimation, in terms of long execution time and the crash of the software execution engine (SEE). We proposed to balance the workload among servers through web services and we also limited the number of dummy states in order to address the high computational costs. Such hybrid non-threaded R2R has been successfully deployed in one of the most critical processes in the high volume production environment, and we have demonstrated its improved process control performance and as well as its robustness.

Threaded, EWMA based non-threaded and the model-based non-threaded R2R controller performances were compared head to head on the same process and the same tool in production, and our data collection showed that the model-based non-threaded controller outperforms the other two control methods, the EWMA non-threaded and the threaded R2R controls. The data collection was done with the threaded control in the “active” mode, which means it actively controlled the process, and the other two non-threaded R2R controllers were in the “passive” mode. Such a framework allows us to compare the performance of controllers in real time, so an automatic on-line tuning of the non-threaded R2R control can be realized.

7.1.2 Etch Rate Prediction of Silicon Dioxide Film in Diluted HF Solution

Virtual metrology is commonly built upon traditional statistical regression models, such as PLS and neural networks. Since the chemistry of etching silicon dioxide in the diluted HF solution is well described in the literature, in this research project, we incorporated physics and chemical reaction models into the virtual metrology. The comparison between traditional PLS regression model and the multiphysics-based model suggested that the multiphysics-based model is a better method to select key process variables for the VM model and it is a better method

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to establish meaningful process indicators. We also discovered more advantages of a multiphysics-based model. For instance, it requires less training data because of its fast convergence and it can account for variations of chemical and gas batches and VM prediction accuracy can be biased or compromised without taking account of those.

The VM data can be fed into R2R controllers. For example, VM thickness predictions can be used as a feed-forward component of a wafer level dry etch R2R controller downstream to improve wafer level variations. In our research, we used the VM system in a different way where VM is used to update the R2R model. An R2R control model, typically the slope, is usually fixed, so the R2R control performance may be downgraded after process conversions at the current step or the upstream steps. A frequent model update through the VM system ensures the optimal state, especially when there is a feed-forward component.

We have demonstrated in high volume production that the multiphysics-based VM model produces better prediction quality than the traditional statistical models. The predicted etch rate is also used to update process gains of the R2R controller, so that the R2R controller compensates feed-forward disturbance better. The process control was still capable after a 50% sampling rate reduction. This project has realized almost all the VM benefits, which include excursion prevention, yield improvement, process capability gain, cycle time and the cost reduction.

7.1.3 A Generic Diffusion Furnace Virtual Metrology

No matter how well a furnace R2R controller performs, the thickness profile exists because of the design of the heaters and the gas depletion effect. Such thickness profile introduces variations into semiconductor manufacturing. We developed a multiphysics model through the equipment knowledge and the design of experiment. Five Gaussian curves and one intercept term are used to produce the final thickness profiles. If the shape of the thickness profile changes over time, then the peak magnitudes of the five Gaussian curves can be updated through metrology data. In the case that the shape of the thickness profile remains the same, only the intercept term is to be updated when new metrology becomes available. To get rid of nonlinear complexity, we propose to assume that the standard deviation terms

remain the same, and such an approximation is acceptable, because the standard deviations are “mainly” determined by design of heaters such as the length of the heater, which is fixed. Such assumptions have made the state estimation much simpler.

Two difficulties, queue time effects and the R2R control adjustment, were discovered when we deployed this strategy in the actual production environment. An offset curve or model was established to solve queue time problem. On the other hand, R2R adjustments can be modeled or offset by adjusting the peak value of the Gaussian curves or the intercept term. We obtained excellent prediction results after solving these problems.

One of the major benefits of this project is feeding forward diffusion thickness profile data to a wafer level R2R control at the downstream process steps, so wafer level variation caused by different furnace positions can be reduced or removed. The wafer level dry etch R2R controller application was proposed in literature, while we propose to extend this methodology to an ion implantation step, which will be discussed in detail in Section 7.2.

7.1.4 Oxygen Plasma Resist Descum Virtual Metrology

As of today, the plasma physics and phenomenon are not well understood and this makes virtual metrology of dry etch very challenging. We studied the major characteristics of O₂ plasma resist descum process including the sodium contamination free, electrical charge and radiation free through the downstream plasma and the etch rate improvement by introducing a forming gas H2O2. Model

parameters were selected through the background of physics and chemical reac- tions. First we invented a new method called “Zonal” data analysis, which can improve the prediction quality of a PLS model by almost 50%. Three recursive PLS model update methods were simulated on the same data set, and we concluded that intercept update performs best, because it can adapt to the incoming variations.

We created a new process indictor through the extensive chemical reaction and process knowledge to improve the multiphysics-based etch rate model. The model parameters were obtained through a DOE. Based on the experience of the PLS evaluations, the model update is through the intercept state estimation,

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which captures the drift of incoming variations. A r2 = 0.708 was achieved in the production environment, which is sufficient for real time wafer level etch rate monitoring. The multiphysics-based we used is a simple linear model and we believe that the prediction can be improved further through exploring nonlinear models or handling incoming material variations like resist batch.