In an advanced semiconductor fab, online quality monitoring of wafers is required for maintaining high stability and yield of production equipment. The current practice of only measuring monitor wafers may not be able to timely detect the equipment-performance drift happening in-between the scheduled measurements. This may cause defects of production wafers and, thereby, raise the production cost. In this paper, a novel virtual metrology scheme (VMS) is proposed for overcoming this problem. The proposed VMS is capable of predicting the quality of each production wafer using parameters data from production equipment. Consequently, equipment-performance drift can be detected promptly. A radial basis function neural network is adopted to construct the virtual metrology model. Also, a model parameter coordinator is developed to effectively increase the prediction accuracy of the VMS. The chemical vapor deposition (CVD) process in semiconductor manufacturing is used to test and verify the effectiveness of the proposed VMS. Test results show that the proposed VMS demonstrates several advantages over the one based on back-propagation neural network and can achieve high prediction accuracy with mean absolute percentage error being 0.34% and maximum error being 1.15%. The proposed VMS is simple yet effective, and can be practically applied to construct the prediction models of semiconductor CVD processes.