Browsing by Author "Chang, Wen-Jen"

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A multiple criteria decision for trading capacity between two semiconductor fabs
(2008-10-01)
Wu, Muh-Cherng
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Chang, Wen-Jen
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工業工程與管理學系
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Department of Industrial Engineering and Management
This paper presents a multiple criteria decision approach for trading weekly tool capacity between two semiconductor tabs. Due to the high-cost characteristics of tools, a semiconductor company with multiple fabs (factories) may weekly trade their tool capacities. That is, a lowly utilized workstation in one fab may sell capacity to its highly utilized counterpart in the other fab. Wit and Chang [Wu, M. C., & Chang, W. J. (2007). A short-term capacity trading method for semiconductor fabs with partnership. Expert Systems with Application, 33(2), 476-483] have proposed a method for making weekly trading decisions between two wafer tabs. Compared with no trading, their method could effectively increase the two fabs' throughput for a longer period such as 8 weeks. However, their trading decision-making is based on a single criterion-number of weekly produced operations, which may still leave a space for improving. We therefore proposed a multiple criteria trading decision approach in order to further increase the two fabs' throughput. The three decision criteria are: number of operations, number of layers, and number of wafers. This research developed a method to find an optimal weighting vector for the three criteria. The method firstly used NN + GA (neural network + genetic algorithm) to find an optimal trading decision in each week, and then used DOE + RSM (design of experiment + response surface method) to find an optimal weighting vector for a longer period, say 10 weeks. Experiments indicated that the multiple criteria approach indeed outperformed the previous method in terms the fabs' long-term throughput. (C) 2007 Elsevier Ltd. All rights reserved.
A short-term capacity trading method for semiconductor fabs with partnership
(2007-08-01)
Wu, Muh-Cherng
;
Chang, Wen-Jen
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
This paper presents a capacity trading method for two semiconductor fabs that have established a capacity-sharing partnership. A fab that is predicted to have insufficient capacity at some workstations in a short-term period (e.g. one week) could purchase tool capacity from its partner fab. The population of such a capacity-trading portfolio may be quite huge. The proposed method involves three modules. We first use discrete-event simulation to identify the trading population. Secondly, some randomly sampled trading portfolios with their performance measured by simulation are used to develop a neural network, which can efficiently evaluate the performance of a trading portfolio. Thirdly, a genetic algorithm (GA) embedded with the developed neural network is used to find a near-optimal trading portfolio from the huge trading population. Experiment results indicate that the proposed trading method outperforms two other bench-marked methods in terms of number of completed operations, number of wafer outs, and mean cycle time. (c) 2006 Elsevier Ltd. All rights reserved.
Scheduling a hybrid MTO/MTS semiconductor fab with machine-dedication features
(2008-03-01)
Wu, Muh-Cherng
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Jiang, Jr-Hsiung
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Chang, Wen-Jen
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
A semiconductor foundry is essentially a make-to-order (MTO) factory. Yet, in a low-demand season, it may enter into a hybrid business model-producing make-to-stock (MTS) as well as MTO products to maintain high utilization of machines. This research proposes a scheduling method for such a hybrid MTO/MTS system with machine-dedication characteristics, a constraint imposed on the process route caused by the advance of manufacturing technology. The scheduling method aims to achieve a high on-time delivery rate for MTO products as well as a,high throughput for MTS products. Simulation experiments show that the proposed scheduling method outperforms representative methods in the literature. (C) 2007 Elsevier B.V. All rights reserved.