The construction of production performance prediction system for semiconductor manufacturing with artificial neural networks

被引:36
作者
Huang, CL
Huang, YH
Chang, TY
Chang, SH
Chung, CH
Huang, DT
Li, RK
机构
[1] Natl Chiao Tung Univ, Dept Ind Engn & Management, Hsinchu 30050, Taiwan
[2] Minghsin Inst Technol, Dept Ind Engn & Management, Hsinchu, Taiwan
[3] Mosel Vitel Inc, Prod Control Dept, Hsinchu, Taiwan
关键词
D O I
10.1080/002075499191319
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The major performance measurements for wafer fabrication system comprise WIP level, throughput and cycle time. These measurements are influenced by various factors, including machine breakdown, operator absence, poor dispatching rules, emergency order and material shortage. Generally, production managers use the WIP level profile of each stage to identify an abnormal situation, and then make corrective actions. However, such a measurement is reactive, not proactive. Proactive actions must effectively predict the future performance, analyze the abnormal situation, and then generate corrective actions to prevent performance from degrading. This work systematically constructs artificial neural network models to predict production performances for a semiconductor manufacturing factory. An application for a local DRAM wafer fabrication has demonstrated the accuracy of neural network models in predicting production performances.
引用
收藏
页码:1387 / 1402
页数:16
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