scholarly journals An Iterative Procedure for Optimizing the Performance of the Fuzzy-Neural Job Cycle Time Estimation Approach in a Wafer Fabrication Factory

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Toly Chen ◽  
Yi-Chi Wang
Keyword(s):  
Cycle Time ◽  
Empirical Relationship ◽  
Principal Component ◽  
Time Estimation ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Estimation Performance ◽  
Job Classification ◽  
Fuzzy Neural ◽  
Propagation Network

Estimating the cycle time of each job in a wafer fabrication factory is a critical task to every wafer manufacturer. In recent years, a number of hybrid approaches based on job classification (either preclassification or postclassification) for cycle time estimation have been proposed. However, the problem with these methods is that the input variables are not independent. In order to solve this problem, principal component analysis (PCA) is considered useful. In this study, a classifying fuzzy-neural approach, based on the combination of PCA, fuzzy c-means (FCM), and back propagation network (BPN), is proposed to estimate the cycle time of a job in a wafer fabrication factory. Since job classification is an important part of the proposed methodology, a new index is proposed to assess the validity of the classification of jobs. The empirical relationship between theSvalue and the estimation performance is also found. Finally, an iterative process is employed to deal with the outliers and to optimize the overall estimation performance. A real case is used to evaluate the effectiveness of the proposed methodology. Based on the experimental results, the estimation accuracy of the proposed methodology was significantly better than those of the existing approaches.

Job remaining cycle time estimation with a post-classifying fuzzy-neural approach in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1021-1031 ◽  
Author(s):  
T Chen
Keyword(s):  
Test Data ◽  
Simulation Study ◽  
Cycle Time ◽  
Estimation Error ◽  
Back Propagation ◽  
Time Estimation ◽  
Wafer Fabrication ◽  
Fuzzy Neural ◽  
Fuzzy Back Propagation Network ◽  
Propagation Network

A post-classifying fuzzy-neural approach is proposed in this study for estimating the remaining cycle time of each job in a wafer fabrication plant, which has seldom been investigated in past studies but is a critical task for the wafer fabrication plant. In the methodology proposed, the fuzzy back-propagation network (FBPN) approach for job cycle time estimation is modified with the proportional adjustment approach to estimate the remaining cycle time instead. Besides, unlike existing cycle time estimation approaches, in the methodology proposed a job is not preclassified but rather post-classified after the estimation error has been generated. For this purpose, a back-propagation network is used as the post-classification algorithm. To evaluate the effectiveness of the methodology proposed, production simulation is used in this study to generate some test data. According to experimental results, the accuracy of estimating the remaining cycle time could be improved by up to 64 per cent with the proposed methodology.

scholarly journals Internal Due Date Assignment in a Wafer Fabrication Factory by an Effective Fuzzy-Neural Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-13
Author(s):  
Toly Chen
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Due Date ◽  
Planning And Scheduling ◽  
Simultaneous Application ◽  
Due Date Assignment ◽  
Fuzzy Neural ◽  
Propagation Network

Owing to the complexity of the wafer fabrication, the due date assignment of each job presents a challenging problem to the production planning and scheduling people. To tackle this problem, an effective fuzzy-neural approach is proposed in this study to improve the performance of internal due date assignment in a wafer fabrication factory. Some innovative treatments are taken in the proposed methodology. First, principal component analysis (PCA) is applied to construct a series of linear combinations of the original variables to form a new variable, so that these new variables are unrelated to each other as much as possible, and the relationship among them can be reflected in a better way. In addition, the simultaneous application of PCA, fuzzy c-means (FCM), and back propagation network (BPN) further improved the estimation accuracy. Subsequently, the iterative upper bound reduction (IUBR) approach is proposed to determine the allowance that will be added to the estimated job cycle time. An applied case that uses data collected from a wafer fabrication factory illustrates this effective fuzzy-neural approach.

Fuzzy-neural-network-based fluctuation smoothing rule for reducing the cycle times of jobs with various priorities in a wafer fabrication plant: A simulation study

2009 ◽  
Vol 223 (8) ◽  
pp. 1033-1043 ◽  
Author(s):  
T Chen
Keyword(s):  
Neural Network ◽  
Cycle Time ◽  
Fuzzy Neural Network ◽  
Optimal Solution ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
The Difference ◽  
Fluctuation Smoothing

This paper presents a fuzzy-neural-network-based fluctuation smoothing rule to further improve the performance of scheduling jobs with various priorities in a wafer fabrication plant. The fuzzy system is modified from the well-known fluctuation smoothing policy for a mean cycle time (FSMCT) rule with three innovative treatments. First, the remaining cycle time of a job is estimated by applying an existing fuzzy-neural-network-based approach to improve the estimation accuracy. Second, the components of the FSMCT rule are normalized to balance their importance. Finally, the division operator is applied instead of the traditional subtraction operator in order to magnify the difference in the slack and to enhance the responsiveness of the FSMCT rule. To evaluate the effectiveness of the proposed methodology, production simulation is applied to generate some test data. According to the experimental results, the proposed methodology outperforms six existing approaches in the reduction of the average cycle times. In addition, the new rule is shown to be a Pareto optimal solution for scheduling jobs in a semiconductor manufacturing plant.

scholarly journals A Novel Fuzzy-Neural Slack-Diversifying Rule Based on Soft Computing Applications for Job Dispatching in a Wafer Fabrication Factory

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Toly Chen ◽  
Richard Romanowski
Keyword(s):  
Soft Computing ◽  
Cycle Time ◽  
Fuzzy Classification ◽  
Wafer Fabrication ◽  
Estimation Accuracy ◽  
Soft Computing Techniques ◽  
Fuzzy Neural ◽  
Job Dispatching ◽  
Neural Network Prediction ◽  
Fluctuation Smoothing

This study proposes a slack-diversifying fuzzy-neural rule to improve job dispatching in a wafer fabrication factory. Several soft computing techniques, including fuzzy classification and artificial neural network prediction, have been applied in the proposed methodology. A highly effective fuzzy-neural approach is applied to estimate the remaining cycle time of a job. This research presents empirical evidence of the relationship between the estimation accuracy and the scheduling performance. Because dynamic maximization of the standard deviation of schedule slack has been shown to improve performance, this work applies such maximization to a slack-diversifying fuzzy-neural rule derived from a two-factor tailored nonlinear fluctuation smoothing rule for mean cycle time (2f-TNFSMCT). The effectiveness of the proposed rule was checked with a simulated case, which provided evidence of the rule’s effectiveness. The findings in this research point to several directions that can be exploited in the future.

A PCA-FBPN Approach for Job Cycle Time Estimation in a Wafer Fabrication Factory

2012 ◽  
Vol 2 (2) ◽  
pp. 50-67 ◽  
Author(s):  
Toly Chen
Keyword(s):  
Cycle Time ◽  
Back Propagation ◽  
Principal Component ◽  
Time Estimation ◽  
Cycle Times ◽  
Forecasting Performance ◽  
Semiconductor Manufacturer ◽  
Fuzzy Back Propagation Network ◽  
Propagation Network

Variable replacement is a well-known technique to improve the forecasting performance, but has not been applied to the job cycle time forecasting, which is a critical task to a semiconductor manufacturer. To this end, in this study, principal component analysis (PCA) is applied to enhance the forecasting performance of the fuzzy back propagation network (FBPN) approach. First, to replace the original variables, PCA is applied to form variables that are independent of each other, and become new inputs to the FBPN. Subsequently, a FBPN is constructed to estimate the cycle times of jobs. According to the results of a case study, the hybrid PCA-FBPN approach was more efficient, while achieving a satisfactory estimation performance.

A FUZZY-NEURAL FLUCTUATION SMOOTHING RULE FOR SCHEDULING JOBS WITH VARIOUS PRIORITIES IN A SEMICONDUCTOR MANUFACTURING FACTORY

2009 ◽  
Vol 17 (03) ◽  
pp. 397-417 ◽  
Author(s):  
TOLY CHEN ◽  
YU-CHENG LIN
Keyword(s):  
Cycle Time ◽  
Semiconductor Manufacturing ◽  
Optimal Solution ◽  
Time Estimation ◽  
Pareto Optimal Solution ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Cycle Times ◽  
Fuzzy Neural ◽  
Fluctuation Smoothing

A fuzzy-neural fluctuation smoothing rule is proposed in this study to improve the performance of scheduling jobs with various priorities in a semiconductor manufacturing factory. The fuzzy-neural fluctuation smoothing rule is modified from the well-known fluctuation smoothing rule by improving the accuracy of estimating the remaining cycle time of a job, which is done by applying Chen's fuzzy-neural approach with multiple buckets. To evaluate the effectiveness of the proposed methodology, production simulation is also applied in this study. According to experimental results, incorporating a more accurate remaining cycle time estimation mechanism did improve the scheduling performance especially in reducing the average cycle times. Besides, the fuzzy-neural fluctuation smoothing rule was also shown to be a Pareto optimal solution for scheduling jobs with various priorities in a semiconductor manufacturing factory.

A fuzzy-neural system with error feedback to adjust classification for forecasting wafer lot flow time: A simulation study

2007 ◽  
Vol 221 (5) ◽  
pp. 807-817 ◽  
Author(s):  
T Chen ◽  
Y-C Wang
Keyword(s):  
Back Propagation ◽  
Neural System ◽  
Estimation Accuracy ◽  
Forecasting Accuracy ◽  
Forecasting Error ◽  
Fuzzy Neural ◽  
Suitable Combination ◽  
Flow Cycle ◽  
Generate Test Data ◽  
Propagation Network

Estimating lot flow (cycle) time is a critical task for a wafer fabrication plant (wafer fab). Many recent studies have shown that pre-classifying wafer lots before estimating the flow times is beneficial to estimation accuracy. In this aspect, various classification approaches, e.g. k-means (kM), fuzzy c-means (FCM), and self-organization map (SOM), have been applied. After pre-classification, to estimate the flow times for lots belonging to different categories, different approaches (that are in fact the same approaches but with different parameter settings) are applied. However, these applications of classification approaches considered only the data of wafer lots, but ignored whether the classification approaches combined with the subsequent estimation techniques were suitable for the data. To tackle this problem, instead of trying many possible classification and forecasting approaches to find out the most suitable combination, a FCM and back propagation network (BPN) combination is chosen in the current study. In the proposed methodology, the classification results by FCM will be adjusted with forecasting error fed back from the BPN. In this way, if the FCM-BPN combination is not good enough for the data, then a forecasting error will be generated and fed back to the FCM classifier to adjust the classification results. After some replications, the FCM-BPN combination will become more suitable for the data. To evaluate the effectiveness, production simulation is applied in the present study to generate test data. According to experimental results, the forecasting accuracy of the proposed methodology is significantly better than those of many existing approaches. The effects of adjusting classification results with prediction error are also revealed.

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