Selection Schemes of Dual Virtual-Metrology Outputs for Enhancing Prediction Accuracy

Abstract

Selection schemes between neural-network (NN) and multiple-regression (MR) outputs of a virtual metrology system (VMS) are studied in this paper. Both NN and MR are applicable algorithms for implementing virtual-metrology (VM) conjecture models. A MR algorithm may achieve better accuracy only with a stable process, whereas a NN algorithm may have superior accuracy when equipment property drift or shift occurs. To take advantage of both MR and NN algorithms, the simple-selection scheme (SS-scheme) is first proposed to enhance the VM conjecture accuracy. This SS-scheme simply selects either NN or MR output according to the smaller Mahalanobis distance between the input process data set and the NN/MR-group historical process data sets. Furthermore, a weighted-selection scheme (WS-scheme), which computes the VM output with a weighted sum of NN and MR results, is also developed. This WS-scheme generates a well-behaved system with continuity between the NN and MR outputs. Both the CVD and photo processes of a fifth-generation TFT-LCD factory are adopted in this paper to test and compare the conjecture accuracy among the solo-NN, solo-MR, SS-scheme, and WS-scheme algorithms. One-hidden-layered back-propagation neural network (BPNN-I) is applied to establish the NN conjecture model. Test results show that the conjecture accuracy of the WS-scheme is the best among those solo-NN, solo-MR, SS-scheme, and WS-scheme algorithms.