An Artificial Neural Network Model As Related To The Study Of The Wafer Demand Forecasting In The Semiconductor Industry

• Main Authors: Chen, Jing-Heuy, 陳靖惠
• Other Authors: Chen, An-Pin
• Format: Others
• Language: zh-TW
• Published: 1994
• Online Access: http://ndltd.ncl.edu.tw/handle/13766622550160295887

碩士 === 國立交通大學 === 資訊管理研究所 === 82 === 　　The Neural Forecasting Model proposed is designed for the uncertainty nature of the wafer demand issue in the semiconductor industry. Currently, the needed primary material, the wafer, is resorted to be imported overseas. The lead-time of purchasing wafer is about 2 to 3 months. Besides, in the Fab, the expensive wafers, with few varieties, should be control under suitable inventory. So, it is urgent for the semiconductor industry to have a precise, simple forecasting method for ordering. 　　In the past, conducting marketing survey has been the most regular method for demand forecasting . But it isn’t satisfactory when the samples aren’t enough or aren’t representative. The wafer demand forecasting in Taiwan is especially effected by factors, such as historical usage amounts, the international marketing environment, seasonal factors etc.. Combining both quantitive and qualitive factors into a forecasting model, this neural forecasting model forms an integrated forecasting computing system. Finally, in this case study, some of the performance of the neural wafer forecasting model is verified with a nearly 0% error rate in one single forecasting, and a 2% error rate in the total of five forecasting periods.