Decision-based virtual metrology for advanced process control to empower smart production and an empirical study for semiconductor manufacturing

Virtual metrology (VM) has been employed to improve the performance of advanced process control for semiconductor manufacturing. A number of VM models have been proposed to predict the quality characteristics for the wafers that have not been sampled and measured. However, little research has been done to address the interrelations between the VM model and associated decisions for advanced process control and yield enhancement. There is a research need for developing a framework that can integrate the confidence level of VM prediction and domain knowledge to derive appropriate decisions for real-time control. To fill the gaps, this study aims to develop a decision-based virtual metrology framework that integrates clustering and regression models to enhance the prediction and ensure the decision quality for the R2R controller. In particular, Isolation Forest is employed to cluster the data group for multi-recipes and multi-tools. Random Forest Regression is developed for the prediction model for each category respectively to enhance the accuracy of predicted results. Furthermore, this approach designs an overall confidence score based on data integrity and predicted results to suggest the optimal decision rules for R2R control in real time. This approach is validated with an empirical study in a leading semiconductor manufacturing company in Taiwan. Indeed, the results have demonstrated practical viability and the developed solution has been implemented.