Development of a Framework for Analyzing Process Monitoring Data with Applications to Semiconductor Manufacturing Process

Abstract

A semiconductor manufacturing process consists of hundreds of steps, and produces a large amount of data. These process monitoring data contain useful information on the behavior of a process or a product. After semiconductor fabrication is completed, dies on a wafer are classified into bins in the EDS (Electrical Die Sorting) process. Quality engineers in semiconductor industry are interested in relating these bin data to the historical monitoring data to identify those process variables that are critical to the quality of the final product. Data mining techniques can be effectively used for this purpose. In this article, a framework for analyzing semiconductor process monitoring and bin data is developed using the data mining and other statistical techniques.