The construction of production performance prediction system for semiconductor manufacturing with artificial neural networks
| Autor: | C. H. Chung, T. Y. Chang, C. L. Huang, Y. H. Huang, D. T. Huang, Sheng-Hung Chang, Rong-Kwei Li |
|---|---|
| Rok vydání: | 1999 |
| Předmět: |
Engineering
Artificial neural network Semiconductor device fabrication business.industry Strategy and Management Management Science and Operations Research Industrial and Manufacturing Engineering Manufacturing engineering Reliability engineering Wafer fabrication Performance prediction Production (economics) Factory (object-oriented programming) business Throughput (business) Dram |
| Zdroj: | International Journal of Production Research. 37:1387-1402 |
| ISSN: | 1366-588X 0020-7543 |
| DOI: | 10.1080/002075499191319 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|