Capacity Determination Model With Time Constraints and Batch Processing in Semiconductor Wafer Fabrication

Tu, Ying-Mei; Liou, Chuen-Shiuan

doi:10.1080/10170660609509008

Cited by 10 publications

(9 citation statements)

References 9 publications

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“…Therefore, a precise capacity calculation is necessary for resolving TC issues. Unfortunately, only a few studies have taken TC issues into consideration (Robinson andGiglio 1999, Tu andLiou 2006). Furthermore, STC issues in the back-end process have not been addressed in those models that consider TC.…”

Section: Introductionmentioning

confidence: 96%

“…Previous studies have indicated that a sufficient protective capacity is the foundation for effectively managing TC issues (Robinson and Giglio 1999, Christe and Wu 2002, Tu and Liou 2006. Abundant capacity, nevertheless, will not be realised in wafer fabrication.…”

Section: Introductionmentioning

confidence: 97%

“…Nonetheless, time constraints (TCs) are not generally addressed in the previous models. A TC is a time window set between two specific operations to prevent undesirable copper film oxidation or fluorine precipitation on wafer surfaces (Robinson andGiglio 1999, Tu andLiou 2006). Wafer yield will decline when wafers stay longer in the clean room.…”

Section: Introductionmentioning

confidence: 99%

“…Fundamentally, good planning at each successive workstation will be necessary to solve TC issues. Tu and Liou (2006) developed a capacity determination model based on a GI/G/c queuing approach to address TC with batch processes. They determined the required capacity of a furnace workstation based on the length of the TC.…”

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confidence: 99%

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Capacity planning with sequential two-level time constraints in the back-end process of wafer fabrication

Chen

2009

International Journal of Production Research

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A time constraint is a queue-time boundary that is set between particular sequential operations to ensure final product yield. These time boundaries, called 'sequential time constraints', can be found in a series of operations on the backend of wafer fabrication. Wafers exceeding the time constraints are traced through the fabrication process, but generally pass through the remaining processes. Nonetheless, it is a waste of capacity to continue processing wafers with unacceptable yield. Unfortunately, these unacceptable wafers cannot be identified before the wafer acceptance test using the current control policy. This work proposes a control rule for two-level time constraints with capacity planning methodology under this rule. Wafers exceeding the lower time constraints will be treated as normal wafers; however, once wafers exceed the upper time constraint, they will be scrapped immediately. In the capacity planning model, a GI/G/m queuing network is applied to determine the required number of machines. By pre-setting target yields, the rates of wafers being marked or scrapped can be controlled. Furthermore, a novel scheme -regarding machine failures as irregular customers -is introduced to describe the effect of service interruptions. The results show that the proposed control rule and capacity planning model can more effectively resolve the issues of sequential time constraints. Moreover, the results of the analysis indicate that the current capacity expansion policy of the semiconductor industry should be re-examined.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Capacity planning with sequential two-level time constraints in the back-end process of wafer fabrication

Chen

2009

International Journal of Production Research

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“…C.-H. Wu et al Tu and Liou (2006) consider a GI/G/m queueing model and develop a capacity planning method in systems with time constraints. Kitamura et al (2006) develop a scrap risk evaluation method in an M/M/1 queueing system with process queue time constraint.…”

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confidence: 99%

Dynamic production control in a serial line with process queue time constraint

Lin

Chien

2009

International Journal of Production Research

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This research examines the production control problem in two-station tandem queueing systems under time constraints. In these two-station tandem queueing systems, jobs must first be processed at the upstream station and then the downstream station. For each job, the sum of the waiting and processing time in the downstream queue is limited by an upper bound. This time constraint is called the process queue time constraint. When the process queue time constraint is violated, a significant scrap cost will be accrued. In this research, we develop a Markov decision model to study the production control problem under process queue time constraints. The objective is to minimise the sum of the expected longrun average inventory holding costs and scrap costs. According to the Markov decision model, an interesting exhaustive structure of the optimal production control policy is found. Based on this exhaustive structure, an efficient algorithm is developed to solve the production control problem numerically. The performance of the proposed algorithm is verified by a simulation study. Compared with other heuristics in the literature, the proposed algorithm can significantly reduce production costs while improving system throughput and utilisation.

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Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing

May,

Oberst,

Lanza

2024

J Intell Manuf

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Continuous product individualization and customization led to the advent of lot size one in production and ultimately to product-inherent uniqueness. As complexities in individualization and processes grow, production systems need to adapt to unique, product-inherent constraints by advancing production control beyond predictive, rigid schedules. While complex processes, production systems and production constraints are not a novelty per se, modern production control approaches fall short of simultaneously regarding the flexibility of complex job shops and product unique constraints imposed on production control. To close this gap, this paper develops a novel, data driven, artificial intelligence based production control approach for complex job shops. For this purpose, product-inherent constraints are resolved by restricting the solution space of the production control according to a prediction based decision model. The approach validation is performed in a real semiconductor fab as a job shop that includes transitional time constraints as product-inherent constraints. Not violating these time constraints is essential to avoid scrap and similarly increase quality-based yield. To that end, transition times are forecasted and the adherence to these product-inherent constraints is evaluated based on one-sided prediction intervals and point estimators. The inclusion of product-inherent constraints leads to significant adherence improvements in the production system as indicated in the real-world semiconductor manufacturing case study and, hence, contributes a novel, data driven approach for production control. As a conclusion, the ability to avoid a large majority of violations of time constraints shows the approaches effectiveness and the future requirement to more accurately integrate such product-inherent constraints into production control.

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Capacity Determination Model With Time Constraints and Batch Processing in Semiconductor Wafer Fabrication

Cited by 10 publications

References 9 publications

Capacity planning with sequential two-level time constraints in the back-end process of wafer fabrication

Capacity planning with sequential two-level time constraints in the back-end process of wafer fabrication

Dynamic production control in a serial line with process queue time constraint

Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing

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