| Summary: | 碩士 === 國立交通大學 === 工業工程與管理系所 === 96 === Process yield is a very important performance index for manufacturing. In order to enhance yield, process data will be automatically or semi-automatically recorded for diagnosing faults. Many production processes often involve hundreds of process and quality parameters. As a result, finding the root causes of process variation becomes a difficult problem. This study combined data mining techniques and statistical methods to develop a solution framework for identifying the critical process parameters. This framework applied 1R algorithm to identify the clue variables, and then used correlation analysis and domain knowledge to find the to-be-controlled variables. Finally, it utilized regression analysis to set the value of the to-be-controlled variables. We validated this framework with an empirical study about the control of thickness variation for polyimide film manufacturing. Results indicated that the process yield has been improved from 41.7 % to 61 %. Moreover, we compared four versions of 1R with four versions of C4.5, and our initial results show the promise of 1R algorithm to improve the process variation.
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