TY - JFULL AU - Seung Hwan Park and Cheng-Sool Park and Jun Seok Kim and Youngji Yoo and Daewoong An and Jun-Geol Baek PY - 2014/3/ TI - Pattern Recognition Using Feature Based Die-Map Clusteringin the Semiconductor Manufacturing Process T2 - International Journal of Industrial and Manufacturing Engineering SP - 321 EP - 326 VL - 8 SN - 1307-6892 UR - https://publications.waset.org/pdf/9997462 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 86, 2014 N2 - Depending on the big data analysis becomes important, yield prediction using data from the semiconductor process is essential. In general, yield prediction and analysis of the causes of the failure are closely related. The purpose of this study is to analyze pattern affects the final test results using a die map based clustering. Many researches have been conducted using die data from the semiconductor test process. However, analysis has limitation as the test data is less directly related to the final test results. Therefore, this study proposes a framework for analysis through clustering using more detailed data than existing die data. This study consists of three phases. In the first phase, die map is created through fail bit data in each sub-area of die. In the second phase, clustering using map data is performed. And the third stage is to find patterns that affect final test result. Finally, the proposed three steps are applied to actual industrial data and experimental results showed the potential field application. ER -