Constructing a yield model for integrated circuits based on a novel fuzzy variable of clustered defect pattern

Abstract

As the wafer size increases, the clustering phenomenon of defects becomes significant. In addition to clustered defects, various clustering patterns also influence the wafer yield. In fact, the recognition of clustering pattern usually exists fuzziness. However, the wafer yield models in previous studies did not consider the fuzziness of clustering pattern belonging to which shape in recognition. Therefore, the objective of this study is to develop a new fuzzy variable of clustering pattern (FVCP) by using fuzzy logic control, and predict the wafer yield by using back-propagation neural network (BPNN) incorporating ant colony optimization (ACO). The proposed method utilizes defect counts, cluster index (CI), and FVCP as inputs for ACO-BPNN. A simulated study is utilized to demonstrate the effectiveness of the proposed model.