Abstract
Cycle time management plays an important role in improving the performance of a wafer fabrication factory. It starts from the estimation of the cycle time of each job in the wafer fabrication factory. Although this topic has been widely investigated, several issues still need to be addressed, such as how to classify jobs suitable for the same estimation mechanism into the same group. In contrast, in most existing methods, jobs are classified according to their attributes. However, the differences between the attributes of two jobs may not be reflected on their cycle times. The bi-objective nature of classification and regression tree (CART) makes it especially suitable for tackling this problem. However, in CART, the cycle times of jobs of a branch are estimated with the same value, which is far from accurate. For these reason, this study proposes a joint use of principal component analysis (PCA), CART, and back propagation network (BPN), in which PCA is applied to construct a series of linear combinations of original variables to form new variables that are as unrelated to each other as possible. According to the new variables, jobs are classified using CART before estimating their cycle times with BPNs. A real case was used to evaluate the effectiveness of the proposed methodology. The experimental results supported the superiority of the proposed methodology over some existing methods. In addition, the managerial implications of the proposed methodology are also discussed with an example.
Highlights
Wafer fabrication is a complex and time-consuming process
Chen [13] and Chang and Hsieh [14] have constructed back propagation networks (BPNs) to estimate the cycle time of a job based on the attributes of the job and the current factory conditions
Cycle time estimation, which is considered as a prerequisite for achieving that, has received much attention
Summary
Wafer fabrication is a complex and time-consuming process. First, photoresist patterns are photo-masked onto the surface of a wafer. Chen [13] and Chang and Hsieh [14] have constructed back propagation networks (BPNs) (or feed-forward neural networks, FNNs) to estimate the cycle time of a job based on the attributes of the job and the current factory conditions These studies indicated that linear methods are incapable of estimating the cycle time of a job, which supported the application of nonlinear methods such as ANNs. In addition, to improve the effectiveness of an ANN approach, classifying jobs before (or after) estimating the cycle times have been shown to be a viable strategy. This study proposes a hybrid principal component analysis (PCA), CART, and BPN approach to estimate the cycle time of a job in a wafer fabrication factory. Some methods are easy to use because there have been a lot of software that can seamlessly complete all the necessary steps
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