WIP evolution of a semiconductor factory after a bottleneck workcenter breakdown

Rose, Oliver

doi:10.1109/wsc.1998.745805

Cited by 34 publications

(15 citation statements)

References 2 publications

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“…Define a state variable S(t) for the remaining amount of the anticipatory input. Therefore, S(0)=s, S(T)=0 and S(t) = -E(t) ... (9) In this section, a manufacturing chain of two nodes is used to describe a basic model of optimal control. Let state variable x be [WI, W2, 51T and control variable u be [ a1 , a2 ,EIIT.…”

Section: Model Formulationmentioning

confidence: 99%

“…Simulation has been used to investigate the effect of some disruption events. Rose constructed a simplified plant model of simulation and investigated the behaviour of wafer fabrication when a large quantity of work-in-process suddenly emerges [9]. His plant model is made up of bottleneck workstations as well as a time delay unit that represents all other non-bottleneck stations.…”

Section: Introductionmentioning

confidence: 99%

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Managing Dynamic Events in Full-Load States of Semiconductor Manufacturing Chains

Huang

Chang

Chou

2006

2006 IEEE International Symposium on Semiconductor Manufacturing

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Semiconductor manufacturing is a complicated operation that is plagued by all sorts of uncertainties in process yield, equipment reliability, IC demands and product mixes. This paper describes a new proactive planning method for responding to dynamic events that pose a threat to degenerate the supply services of manufacturing chains. A dynamic system model for manufacturing chains under exogenous demand shocks is first described. As the impact of dynamic events is the most severe when manufacturing chains are in the full-load state, a procedure is next presented for constructing full-load production functions of flexible capacity. The third part of this paper develops an optimal control model for integrating the production functions of multiple production units. Given an inordinary event of demand shock, this model can be used to determine if the shock can be absorbed by the manufacturing chains without degrading its fulfilment services. Numerical examples are given to demonstrate the working ofthe method.

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Section: Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Managing Dynamic Events in Full-Load States of Semiconductor Manufacturing Chains

Huang

Chang

Chou

2006

2006 IEEE International Symposium on Semiconductor Manufacturing

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“…The offline scheduling is also referred to as static or pre-runtime scheduling. The offline analysis of semiconductor front-end has been widely reported [14][15][16]. But, the real challenge lies in the application of online simulation [17] in complex real life applications, as the offline analysis can become very inaccurate over the longer term because of not incorporating unforeseen events such as machine failures, hot jobs, etc.…”

Section: Online Simulation In Cssmentioning

confidence: 99%

Conjunctive simulated scheduling

Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Sivakumar

2004

Int J Adv Manuf Technol

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The scheduling of highly complex job shops, such as those found in semiconductor manufacturing, involves multiple types of work centers, large and changing varieties of the products, sequence dependent set up times, reentrant process flow, dynamically changing environment, etc. These complexities make the job shop scheduling problem very strongly nondeterministic polynominally (NP)-hard and intractable to solve by the traditional scheduling approaches. This paper presents the concept and applications of the conjunctive simulated scheduling (CSS) approach, which incorporates the benefits of discrete event simulation in scheduling the shop and avoids the NP-hard formulation of these complex scheduling problems. The differences between CSS and typical simulation studies and its application to online simulation are also presented.

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“…However, a higher WIP level will prolong cycle time and damage on-time-delivery performance. Also, WIP occupies spaces and requires material handling and control resources (Wein 1992, Askin and Krisht 1994, Rose 1998, Tu and Li 1998. Thereby, holding a proper WIP level is quite critical for backend factories to maintain performance.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these methods focused on the determination of WIP level in fab production environments (Gross and Harris 1974, Wein 1992, Kuroda and Kawada 1995, Robinson et al 1990, Rose 1998, Lin and Lee 2001, Nye 2001 …”

Section: Introductionmentioning

confidence: 99%

Multi-objectives exception management model for semiconductor back-end environment under turnkey service

Guo

Chiang

Pai

2007

Production Planning & Control

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On-time delivery is the need for customer satisfaction which is a critical factor in the survival of the semiconductor industry. The on-time-delivery performance of the whole semiconductor turnkey service depends on the performance of the back-end factories. Unfortunately, undesirable and inevitable production variations make it difficult to maintain and improve a factory's performance and more objectives such as cycle time, throughput rate and the due-date accuracy need to be simultaneously considered. This paper presents an exception management model in order to compromise the contradicting needs of delivery accuracy, throughput rate and cycle time. The exception model can be divided into three parts: (1) an integrated performance index is proposed to compromise multiple performance measures;(2) an AWDL (available WIP deviation level) determination model is designed to gather proper AWDLs for triggering exceptions and (3) a WIP correction action is proposed to make abnormal WIP levels back to normal levels as soon as possible. To evaluate the proposed WIP exception management model, a simulation model is constructed and experiments are then conducted. The simulation results show that the proposed model helps back-end factories to set proper exception triggering conditions, reduce uncertainty occurrences and achieve better performances on due dates.

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WIP evolution of a semiconductor factory after a bottleneck workcenter breakdown

Cited by 34 publications

References 2 publications

Managing Dynamic Events in Full-Load States of Semiconductor Manufacturing Chains

Managing Dynamic Events in Full-Load States of Semiconductor Manufacturing Chains

Conjunctive simulated scheduling

Multi-objectives exception management model for semiconductor back-end environment under turnkey service

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