author Jaewon Park 
2nd author / Hyoung-Gon Lee (Presenter) (Dept. of Industrial Engineering, Seoul National University)
/ Jinwoo Park (Dept. of Industrial Engineering, Seoul National University) 
info Date: 2005년 03월 14일 ~ 2005년 03월 15일
City: Seoul
Nation: Korea
Additional Information: 
year 2005 
category IC 
start / end date  
city / nation  
학회 KGW 
keywords wafer fabrication, due-date assignment, neural network 
abstract Assigning due date and delivering timely are an important factor determining the competitive strength of a manufacturing company. Once an order received, we should check the inventory status before anything else. If the stock runs short, we should make the product. In this case, predicting the cycle time of the product is important for the due date assignment.
Due to the complicated process flow of wafer fabrication, distinctive approaches that differ from common scheduling method are required. Especially, more than one thousand different products might be produced simultaneously in wafer fabrication facilities producing multiple product types like ASIC or power semiconductor. Besides, each product type has different process flow and product types and layers that can be processed together in each batch processing equipment are predetermined. Because of Such characteristics, estimating the flow time of each product is very difficult task.
In this study, we propose a method to predict the cycle time and assign the due date as accurately as possible even if the product mix varies continuously. For this, we group the products according to the similarity of process flow and use the information from the groups to predict the cycle time using artificial neural network. The experimental results show that the proposed method is superior to common due date assignment method. 
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