Using Data Mining Technology to Design an Intelligent CIM System for IC Manufacturing

Abstract

2 Literature Review y all data required data processing. These ata conversion into the data bstract:-The aims of this paper is to depict an intelligent quality analysis control system involved in using dat arehouse, data mining, decision tree, and Bayesian classification analysis to discover the main inconsistency asons in the manufacturing process of semiconductor packaging plants and compare the correctness of assification analysis of the two methods, so as to set up an intelligent quality analysis control system providing efficiency tool for analyzing problems, with a view to identifying the causes of problems, making decision mediately, and eventually reducing the cycle time taken to solve quality-related problems. he contributions of this research are illustrated as follows. Predictions made by the target group by means o ecision tree analysis are more accurate than those made by Bayesian classification, indicating that decision tree alysis is an effective mean of classification analysis in semiconductor packaging quality problems, whereas aluation of feasible methods by data warehouse, and data mining followed by establishment of the basis for a uality analysis system environment, that is characteristic of knowledge sharing may be applied to analysis of the uality problems in all corporation.

A Review of Data Mining Applications in Semiconductor Manufacturing

“…Analyzing this table, one can see that most contributions address yield management and failure detection issues (see [135][136][137][138][139][140][141][142][143][144][145]). The authors from [146] aim at the same problem, but focus on the development of a computer integrated manufacturing (CIM) system to improve product yield. Other articles provide isolated contributions.…”