Data-Driven Adaptive Virtual Metrology for Yield Prediction in Multibatch Wafers

Virtual metrology (VM) is widely used for yield management and control in semiconductor manufacturing owing to its high real-time inspection, low cost, and convenient maintenance. However, the multimodal characteristics of batch processes are ignored in the existing yield VM models. The adaptive multimodal division and modal sample imbalance have also not been considered. Therefore, a data-driven adaptive VM model based on the Gath-Geva fuzzy clustering (GGFC) and multitask learning deep belief network (MLDBN) is proposed to solve the problems above. First, the GGFC model is designed to realize the feature extraction of the batch direction and the modal division of the time and variable directions. Second, the partition coefficient and classification entropy indexes are designed to determine the modal categories automatically and establish the SMOTE model to deal with the imbalance of multimodal samples. Third, the local features of multimodal are extracted by the designed MLDBN models. After that, the batch direction features and the multi-features extracted from multimodal are used as the improved MLDBN parameters to realize the fusion prediction. Finally, experiments are carried out by the accurate industrial data from a multibatch wafer fabrication process. The efforts show that the proposed VM model presents better performances in the result of different indicators and has higher accuracy and robustness than the traditional models.