Rule-based data mining for yield improvement in semiconductor manufacturing
Applied Intelligence
Journal of Intelligent Manufacturing
| Hi-index | 0.00 |
In recent year, because of the professional teamwork, to improve the qualification percentage of products, to accelerate the acknowledgement of product defects and to find out the solution, the LCD driver IC packaging factories have to establish an analysis mode for quality problems of product for more effective and quicker acquisition of needed information and to improve the customer's satisfaction for information system. The past information system used neural network to improve the yield rate of production. In this research employs the star schema of data warehousing as the base of line analysis, and uses decision tree in data mining to establish a quality analysis system for the defects found in the production processes of package factories in order to provide an interface for problem analysis, enabling quick judgment and control over the cause of problem to shorten the time solving the quality problem. The result of research shows that the use of decision tree algorithm reducing the numbers of defected inner leads and chips has been improved, and using decision tree algorithm is more suitable than using neural network in quality problem classification and analysis of the LCD driver IC packaging industry.