Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM–FBPN-ensemble approach with multiple buckets and partial normalization

Chen, Tin-Chih; Wang, Yi‐Chi; Tsai, Horng Ren

doi:10.1007/s00170-008-1665-4

Cited by 65 publications

(33 citation statements)

References 16 publications

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“…The forecasting error was further magnified (squared) in calculating the RMSE.4 Conversely, using a BPN alone did not achieve fair performance in this experiment. Even the BPN could describe the non-linear fluctuations pretty well, it might over-react when there was an unapparent trend(Chen et al, 2009b).5 As expected, the forecasting accuracy of ARIMA was also very good and quite close to that of the collaborative fuzzy-neural approach.…”

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confidence: 63%

A collaborative fuzzy-neural approach for forecasting the price of a DRAM product

Chen¹

2011

IJTIP

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This paper presents a collaborative fuzzy-neural approach for accurately and precisely forecasting the price of a Dynamic Random Access Memory (DRAM) product, which is considered as one of the most important semiconductors widely used in various applications. In the collaborative fuzzy-neural approach, some experts from the application domain are invited to form a council. For some aspects, these experts put forward different points of view. These views are incorporated into the Back Propagation Network and Nonlinear Programming (BPN-NP) approach, and result in different fuzzy-valued price forecasts. To derive a single representative value from these fuzzy price forecasts, the Fuzzy Intersection and Radial Basis Function network (FI-RBF) approach is employed. The effectiveness of the collaborative fuzzy-neural approach is validated with a real example containing the 256-day price data of a 1G DRAM product.

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supporting

confidence: 63%

A collaborative fuzzy-neural approach for forecasting the price of a DRAM product

Chen¹

2011

IJTIP

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“…Past studies (e.g., [21][22][23][24]) have shown that the accuracy of the remaining cycle time forecasting can be improved by job classification. Soft computing methods (e.g., [3,20,25,26]) have received much attention in this field.…”

Section: Methodsmentioning

confidence: 99%

A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory

Chen

2012

Applied Computational Intelligence and Soft Computing

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A nonlinear programming and artificial neural network approach is presented in this study to optimize the performance of a job dispatching rule in a wafer fabrication factory. The proposed methodology fuses two existing rules and constructs a nonlinear programming model to choose the best values of parameters in the two rules by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several studies. In addition, a more effective approach is also applied to estimate the remaining cycle time of a job, which is empirically shown to be conducive to the scheduling performance. The efficacy of the proposed methodology was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future.

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“…Our survey showed that there are only a few works that consider four or more process characteristics in modelling the ramp-up (cf. Wilhelm and Sastri 1979;Chen, Wang, and Tsai 2009). Future research could therefore concentrate on developing planning models that provide a more realistic picture of the ramp-up.…”

Section: Research Opportunitiesmentioning

confidence: 97%

“…If an action has a positive effect on the performance measures, the production planner may continue to apply this action, while a negative impact of the action on the performance measures may induce him/her to change his/her actions. Chen, Wang, and Tsai (2009) developed a model that helps to predict cycle time in semiconductor manufacturing. The approach consists of two phases: in the first phase, the capacity requirement for each lot that is released into the system during the ramp-up is determined.…”

Section: Workflow Managementmentioning

confidence: 99%

Decision support models for production ramp-up: a systematic literature review

Glock

Grosse

2015

International Journal of Production Research

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Production ramp-up is a critical step in the life cycle of a new product, and efficiently managing ramp-ups is a key to business success and market leadership. To support the planning of ramp-ups in practice, researchers have developed decision support models in the past that help to solve problems that arise during the ramp-up phase, such as lot sizing, the assignment of workers to workplaces or the determination of the capacity of the production equipment. Decision support models for production ramp-up typically consider the specific characteristics of this phase, such as uncertainty, growth in demand, worker learning or imperfect production processes. The aim of this paper is to provide a comprehensive overview of decision support models for production ramp-up and to identify areas where more research is needed. First, the paper develops a conceptual framework of production ramp-up by categorising typical planning problems and process characteristics of the ramp-up phase. Secondly, a systematic literature review with a focus on mathematical planning models for the ramp-up phase is conducted. The analysis shows that various decision support models that help to realise an efficient production ramp-up exist, but that there are still many opportunities for future research in this area.

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Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM–FBPN-ensemble approach with multiple buckets and partial normalization

Cited by 65 publications

References 16 publications

A collaborative fuzzy-neural approach for forecasting the price of a DRAM product

A collaborative fuzzy-neural approach for forecasting the price of a DRAM product

A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory

Decision support models for production ramp-up: a systematic literature review

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