Abstract
In semiconductor manufacturing, yield learning is the most critical issue for process improvement. Speeding up the yield learning period can reduce the production cost and enhance the business profit. There are many papers which discussed the variables affecting yield（i.e. scheduling, dispatching, cycle time control, operator education…etc.）, but few studies have quantified the impact of the cycle time and the quantity on the yield learning rate based on the historical data of an existing wafer fabrication facility (fab). In this study, we have constructed a hybrid model using Taguchi Methods and artificial neural networks to find the relationship between the yield learning rate and the related variables (cycle time, product quantity, and production priority) within different stages of a fab life cycle process. Furthermore, the grey system theorem is also applied in order to compare the accuracy and to avoid the drawback in the explanation of the hybrid model. We can use the results from the analysis of the hybrid model to get insight into the impact of cycle time and product quantity of different production priority on the yield learning rate of a fab life cycle process. Furthermore, by incorporating the hybrid model and cost function to evaluate the financial benefit of different combination of variable conditions, the optimal hot lot ratio and the optimal capacity ramp-up speed can be found. Based on the result of the qualitative analysis and the quantitative application, it can reduce the time for process improvement and achieve an ideal yield learning rate for a fab.