Optimization of a cycle time and utilization in semiconductor test manufacturing using simulation based, on-line, near-real-time scheduling system

Sivakumar, Appa Iyer

doi:10.1145/324138.324467

Cited by 22 publications

(18 citation statements)

References 24 publications

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“…Cycle-time prediction is also related to job scheduling (Sivakumar, 1999;Tsai & Chen, 2013), due-date assignment (Joseph & Sridharan, 2011;Mor et al, 2013), and avail-to-promise (ATP) calculation (Oracle, 2014). With the improvement of prediction accuracy in cycle times, the obstacle of determining the range of a cycle time gradually emerges.…”

Section: Introductionmentioning

confidence: 99%

A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory

Chen

2015

J Intell Manuf

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Abstract. Estimating the cycle time of each job in a wafer fabrication factory is critical.An equally critical problem is to estimate the range of a cycle time. This topic has rarely been discussed because most existing methods for range calculation establish only a symmetric range. However, a symmetric range does not meet the requirements of managerial applications based on the lower and upper bounds of a cycle time. Recently, a few researchers have attempted to establish the asymmetric bounds of a cycle time.However, these methods either have overly complex computations or do not consistently perform well. This study proposes a new approach for effective cycle-time bounding.First, construction of a back propagation network predicts the cycle time of a job. Second, two linear functions of the output are formed, and the threshold on the output node is fuzzified to derive the lower and upper bounds of the cycle time. In theory, such a treatment tightens the lower and upper bounds and improves the forecasting precision.Third, theorems for the feasibility of the two linear functions are proved. Fourth, a cloud computing scheme is proposed to improve the bounds in an effective manner. Finally, a real case illustrates the applicability of the proposed methodology. Experimental results show that this methodology narrows the range of cycle times for untrained data by 31%, while maintaining a considerably high hit rate of 92.5%.2

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Section: Introductionmentioning

confidence: 99%

A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory

Chen

2015

J Intell Manuf

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“…The architecture for a generic data driven simulator [13] utilises a simulation model that deploys data from a neutral shop data file. Sivakumar [14] connects ERP/MES data to optimise scheduling integrating the shop-floor data, and allowing the user to enter manually non-system data. Fraunhofer IPA developed a method for web-based configuration and simulation of production systems [15] concerning only the conceptual phase of new production systems.…”

Section: Related Workmentioning

confidence: 99%

Integration of Factory Planning and ERP/MES Systems: Adaptive Simulation Models

Mertins

Rabe

Gocev

Lean Business Systems and Beyond

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Abstract. The purpose of this paper is to demonstrate the approach of a scalable, flexible and adaptive simulation model used in factory planning on the basis of existing ERP and MES data. The dynamic simulation model was developed to validate and verify the changes of the production yield during factory planning and restructuring in the shop floor. This includes machinery relocation and ramp-up, new product phase-in, product portfolio changes and new product qualification processes. The objective is to enable an industrial engineer without simulation knowledge and experience to perform the simulations. Moreover, the model facilitates an analysis of the results for different scenarios, using the actual data from the ERP and MES systems.

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“…During the last 30 years, the performance of a large number of the priority dispatch rules has been studied extensively using simulation techniques (Jones and Fabelo 1998). Some complex dispatching rules comprise multiple levels of heuristics or priorities, with look ahead capability (Sivakumar 1999;Chong, Sivakumar, and Gay 2002).…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we described a two-pass bottleneck scheduling approach on our earlier work of simulationbased scheduling projects (Sivakumar 1999;Chong, Sivakumar, and Gay 2002). The project was carried out for a test site of a semiconductor manufacturer.…”

Section: Introductionmentioning

confidence: 99%

“…The complexity of semiconductor manufacturing with multiple resource constraints, unique equipment configurations, etc. makes it a very challenging scheduling problem (Sivakumar, 1999;Chong, Sivakumar, and Gay 2002). Simulation based scheduling approaches are well suited for such scenarios (Jain andChan 1997, Gupta andSivakumar 2002) and have been repotted in use in semiconductor industry (Watt 1998, Sivakumar 1999.…”

Section: Introductionmentioning

confidence: 99%

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Simulation based scheduling using a two-pass approach

Chong

Sivakumar²

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)

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Bottleneck-based scheduling is a popular approach in production scheduling, and it has achieved promising results in industry. To incorporate this approach in discrete event simulation tools is difficult since the approach requires multiple passes, forward and backward, to reach a good solution for the scheduling problem. In this paper, we propose a two-pass scheduling approach using discrete-event simulation that takes bottlenecks into consideration. In the fust pass, a simulation run is performed and bottlenecks are determined. If significant bottlenecks are identified, a second-pass simulation is performed to reduce the loading on bottlenecks through specific scheduling strategies.

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Optimization of a cycle time and utilization in semiconductor test manufacturing using simulation based, on-line, near-real-time scheduling system

Cited by 22 publications

References 24 publications

A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory

A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory

Integration of Factory Planning and ERP/MES Systems: Adaptive Simulation Models

Simulation based scheduling using a two-pass approach

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