Mining semiconductor manufacturing data for productivity improvement ¯¯ an integrated relational database approach

The semiconductor manufacturing process is a very complex process due to the large number of processes, the diverse equipment set, and the complexities associated with its non-linear process flows. Today, management seeks effective real time reporting on different key parameters such as work-in-process (WIP), cycle time, scrap, and tool up-time in an effort to monitor factory performance so that the business goals are met. The complexity of wafer fabrication (fab) processing, however, has necessitated the need for multiple multi-million dollar Computer Integrated Manufacturing (CIM) systems to be implemented in order to collect real time data. CIM systems deployed in semiconductor manufacturing settings capture real time data about every detail of the factory such as equipment throughputs, WIP, and product and equipment history. Most of the data collected, however, tends to be archived rather than used due to the difficulty of data extraction and presentation. The challenge, therefore, is to transform the massive data in the different CIM databases into meaningful information that is easy to access as well as easy to interpret and manipulate. This paper presents an integrated relational database approach for modeling and collecting semiconductor manufacturing data from multiple database systems and transforming the data into useful reports. A summary of the key reports generated is presented as well. These reports help to monitor factory performance by tracking different key metrics. These reports were implemented in one of Motorolaʹs wafer fabs and have contributed significantly in improving factory performance.