In mixed run processes, typical in semiconductor manufacturing and other automated assembly-line type process, products with different recipe will be produced on the same tool. Product based run-to-run control can be applied to improve the process capability. The effect of product-based controller on low frequency products is, however, minimal, due to inability to track tool variations. In first work, we propose a group and product based EWMA control scheme which combines adaptive k-means cluster method and run-to-run EWMA control to improve the performance of low frequency products in the mixed run process. Similar products could be classified into the same group adaptively and controlled by a group EWMA controller. The group controller is updated by both low frequency products and similar high frequency products; so that low frequency products can be improved by shared information from similar large frequency products. However, the high frequency products are controlled by individual product-based EWMA to avoid interference of the low frequency products. The advantages of proposed control scheme are demonstrated by benchmark simulation and reversed engineered industrial applications.
The performance of EWMA RtR controllers is affected by the values of the selected tuning parameter. In practice, the tuning parameter usually remains unchanged, resulting in sub-optimal performance. In second work, we propose an adaptive-tuning method for a G&P EWMA controller to improve the control performance. The G&P EWMA controller is developed for mixed run processes. We show that the optimum tuning parameters for the next run of this G&P EWMA controller are obtained online using a window of historical input–output data. The performance improvement due to the proposed method is demonstrated by a simulation example and an industrial application.
Run-to-Run control algorithms for high-mix semiconductor processes typically require that the initial product state estimates have sufficient accuracy for satisfactory control. In third work, we use historical process data and apply single observation just-in-time adaptive disturbance estimation (JADE) to find the initial product state estimates. Single observation JADE with random selection, high frequency sampling and exclusion of the earliest data from the average is shown to provide satisfactory initial product state estimates. The effect of initial state estimate accuracy is demonstrated by several simulation and industrial data examples. We also provide a method to estimate relative confidence between individual product state estimates, information that may be used to determine assignment of process error between the tool and product state.

In mixed run processes, typical in semiconductor manufacturing and other automated assembly-line type process, products with different recipe will be produced on the same tool. Product based run-to-run control can be applied to improve the process capability. The effect of product-based controller on low frequency products is, however, minimal, due to inability to track tool variations. In first work, we propose a group and product based EWMA control scheme which combines adaptive k-means cluster method and run-to-run EWMA control to improve the performance of low frequency products in the mixed run process. Similar products could be classified into the same group adaptively and controlled by a group EWMA controller. The group controller is updated by both low frequency products and similar high frequency products; so that low frequency products can be improved by shared information from similar large frequency products. However, the high frequency products are controlled by individual product-based EWMA to avoid interference of the low frequency products. The advantages of proposed control scheme are demonstrated by benchmark simulation and reversed engineered industrial applications.
The performance of EWMA RtR controllers is affected by the values of the selected tuning parameter. In practice, the tuning parameter usually remains unchanged, resulting in sub-optimal performance. In second work, we propose an adaptive-tuning method for a G&P EWMA controller to improve the control performance. The G&P EWMA controller is developed for mixed run processes. We show that the optimum tuning parameters for the next run of this G&P EWMA controller are obtained online using a window of historical input–output data. The performance improvement due to the proposed method is demonstrated by a simulation example and an industrial application.
Run-to-Run control algorithms for high-mix semiconductor processes typically require that the initial product state estimates have sufficient accuracy for satisfactory control. In third work, we use historical process data and apply single observation just-in-time adaptive disturbance estimation (JADE) to find the initial product state estimates. Single observation JADE with random selection, high frequency sampling and exclusion of the earliest data from the average is shown to provide satisfactory initial product state estimates. The effect of initial state estimate accuracy is demonstrated by several simulation and industrial data examples. We also provide a method to estimate relative confidence between individual product state estimates, information that may be used to determine assignment of process error between the tool and product state.

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