Two-step simulation method for automatic material handling system of semiconductor fab

Kong, Sunga

doi:10.1016/j.rcim.2006.05.004

Cited by 24 publications

(6 citation statements)

References 15 publications

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“…• 시뮬레이션 모형(Simulation model) : 중요변수를 도출하 여 변수들의 분포와 반송 로직을 모델링하여 실제 상황 과 유사한 모형을 만들어 시뮬레이션을 통해 반송시간을 예측하는 방법이다 (Kong, 2007;DeJong and Wu, 2002). 지금까지 통계적 분석모형(Analytical model)에 대한 연구는 다변량 회귀분석과 가중이동평균 (Lee et al, 2009)을 이용한 접 근이 많았으며, 최근에는 신경망 모형(Neural network model) (Liao and Wang, 2004;Chen, 2009), SVM(Support vector machine) (Shiue, 2009) 등의 데이터마이닝 기법을 이용한 연구들이 활발하게 이 루어지고 있다.…”

Section: 기존 연구unclassified

Prediction Model on Delivery Time in Display FAB Using Survival Analysis

Han¹,

Baek²

2014

Journal of Korean Institute of Industrial Engineers

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In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

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Section: 기존 연구unclassified

Prediction Model on Delivery Time in Display FAB Using Survival Analysis

Han¹,

Baek²

2014

Journal of Korean Institute of Industrial Engineers

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“…Kong (2007) proposes a two-step simulation: the production simulation step is followed by the AMHS simulation step. In the first step, the throughput of the production line can be predicted.…”

Section: Production Constraintsmentioning

confidence: 99%

Simulation of a full 300mm semiconductor manufacturing plant with material handling constraints

Kiba¹,

Lamiable²,

Dauzere-Peres³

et al. 2009

Proceedings of the 2009 Winter Simulation Conference (WSC)

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Generally, simulation models encountered in the literature in the context of semiconductor manufacturing are obtained with many assumptions (aggregations) and from partial model simulation of the facility (also called fab). The results obtained can be satisfactory for certain types of studies but, when precise results and understanding of the detailed behavior of local or global parts of the fab are needed, these simplifications may no longer be adequate. A more detailed model and a full-scale simulation are then needed. This article presents a detailed simulation model of the production system and Automated Material Handling System for a 300 mm semiconductor plant. Some of the studies performed with this model are discussed.

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“…Flexibility can also be defined as the capability or responsiveness to change and there are three dimensions of general importance to a highmix, low-volume manufacturer as follows: mix flexibility (the result of being able to build different types of products using the same production resources), work-force flexibility, and volume flexibility. In some businesses, more innovative competitors are, for instance, embarking firmly on the deployment of even more flexible arrangements so as to sustain mass customization processes [11]. The concept of agility has attracted increasing interest among researchers and professionals in industry.…”

Section: Manufacturing In the Context Of Increasing Demand Variation And Fluctuationmentioning

confidence: 99%

Changing from conveyor to work-cell-based systems to deal with fluctuations in demand

Shishir¹

2009

Proceedings of the Institution of Mechanical Engineers, Part B:

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As consumption patterns become increasingly sophisticated and manufacturers strive to improve their competitiveness, not only by offering higher quality at competitive costs, but also by providing a broader mix of products and keeping the range attractive by launching successive new products, turbulence in the markets has intensified. This has driven leading manufacturers to research the development of alternative production systems that are supposed to enable them to operate more responsively. This paper discusses the trend of abandoning the strategy of relying on factory automation technologies and conveyor-based assembly lines, and shifting towards more human-centred production systems based on autonomous work-cells. This paper starts with a theoretical review, linking the need to cope with today's market turbulence with the issue of nurturing more agile organizations. A general view of the diffusion trend of work-cell-based assembly systems in various industries is then presented.

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Two-step simulation method for automatic material handling system of semiconductor fab

Cited by 24 publications

References 15 publications

Prediction Model on Delivery Time in Display FAB Using Survival Analysis

Prediction Model on Delivery Time in Display FAB Using Survival Analysis

Simulation of a full 300mm semiconductor manufacturing plant with material handling constraints

Changing from conveyor to work-cell-based systems to deal with fluctuations in demand

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