| Summary: | 博士 === 國立清華大學 === 工業工程與工程管理學系 === 98 === The whole industry is facing the challenges of profitability and growth year by year. In particular, high-tech industry, which features complicated manufacturing processes, rapid product and process migration, and limited delivery time to customers, is encountering a tougher challenge than traditional manufacturing industry before. This inevitably induces companies to compete with each other by continuously employing new technologies, increasing yield, and reducing costs. What makes a first-rate company stand out from others is, aside from its scale and technology, its competence in analytics, which is based on manufacturing intelligence. However, the manufacturing intelligence exists in humans, processes, system, and data. These can be used to help manufacturing managers and their staffs with ill knowledge discovery, knowledge inference, and knowledge explanation in operation management. The issue of how to extract manufacturing intelligence (MI) and integrate with enterprise system is increasingly important since advanced fabrication technologies are complicated and interrelated. Thus, most existing studies focus on the functionalities, which are built from information technology (IT) viewpoints. They are subjective, non-objective analyses, and they are also restricted by human experiences. Most IT tool vendors proposed packages but brought only a limited benefit to their clients.
This research describes the development of a process, with a systematic approach, to structure an MI extraction framework in semiconductor manufacturing. The applications based on this framework would lead to changes and improvement in the semiconductor industry. The process to generate the framework is linked with specific manufacturing objectives in developing an analytical system with data mining tools. These frequently-talked objectives during manufacturing include cost reduction, yield enhancement, and productivity improvement. The focus of this research is quality improvement.
The values of proposed approach in constructing MI framework are: (1) integration with business process and objectives; (2) decomposition of the analysis task into 4 level and 3 analytical objectives which can be enabled by IT system; and (3) effectively extract value information for decision. To validate the viability, an empirical study of yield improvement via wafer bin map clustering and classification from an 8” wafer fab is conducted for the proposed framework. The results show a good performance in efficiency improvement and effectiveness as to standardizie the analytical process to engineers.
|
|---|
