On Effectiveness of Transfer Learning Approach for Neural Network-Based Virtual Metrology Modeling

Virtual metrology (VM) technologies have been successfully developed to enable wafer-to-wafer quality monitoring with reduced costs in the semiconductor manufacturing process. VM estimates an inspection task by predicting metrology variables as a function of process variables using prediction models trained with previous wafer records collected from the involved set of equipment. However, it is difficult to obtain accurate prediction models, in particular for a newly adopted equipment set, if the number of wafer records for the target equipment set are insufficient. While collecting more records is time-consuming and costly, the abundant data of related equipment sets are fruitful sources to improve VM modeling for an equipment set. Here, we investigate the effectiveness of a transfer learning approach based on neural networks to circumvent data insufficiency. This approach exploits knowledge acquired from previously established VM models of other equipment sets to improve the VM modeling on the target equipment set. Experimental results of the implementation with neural networks on actual datasets demonstrate its superiority in both aspects of metrology accuracy and computational efficiency compared with a traditional independent learning approach.