Stochastic wire length sampling for cycle time estimation

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.

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A method for cycle time estimation of semiconductor manufacturing toolsets with correlations

Proceedings of the 2009 Winter Simulation Conference (WSC) ◽

10.1109/wsc.2009.5429165 ◽

2009 ◽

Cited By ~ 4

Author(s):

Raha Akhavan-Tabatabaei ◽

Shengwei Ding ◽

J. George Shanthikumar

Keyword(s):

Cycle Time ◽

Semiconductor Manufacturing ◽

Time Estimation

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

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1243/09544054jem1361 ◽

2009 ◽

Vol 223 (8) ◽

pp. 1021-1031 ◽

Cited By ~ 3

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.

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

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.2012040103 ◽

2012 ◽

Vol 2 (2) ◽

pp. 50-67 ◽

Cited By ~ 7

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.

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