Yield prediction approach based on hybrid DBSCAN and FSVM algorithm in semiconductor wafer fabrication

Yield was the key performance indicator in semiconductor wafer fabrication, and its prediction was very important for improving the chip quality and controlling production cost. However, the chip defect was the key factor affecting the yield level. Therefore, a hybrid Fuzzy Support Vector Machine (FSVM) approach based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to yield prediction in semiconductor wafer fabrication was proposed. DBSCAN was used to analyze congeries characteristic of wafer defects, which could obtain input parameters on distribution pattern and density of defect data in the yield prediction model. Aiming at the fuzzy relation between defects and yield, a yield prediction model was constructed by incorporating fuzzy rules into Support Vector Machine (SVM) to improve prediction accuracy. The corresponding improvement measures were analyzed according to the yield prediction result and congeries characteristic of wafer defects. The simulation experiments showed that the proposed yield prediction method had better universality, and the accuracy was significantly higher than the Poisson model and binomial model.