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

0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.08.144 E-mail address: [email protected] As the wafer size increases, the clustering phenomenon of defects becomes significant. In addition to clustered defects, various clustering patterns also influence thewafer 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 todevelop anew fuzzy variable of clustering pattern (FVCP) byusing fuzzy logic control, andpredict 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. 2011 Elsevier Ltd. All rights reserved.