Semiconductor wafer fabrication production planning using multi-fidelity simulation optimisation

Fan Zhang, Jie Song, Yingzhuo Dai and Jie Xu

International Journal of Production Research, 2020, vol. 58, issue 21, 6585-6600

Abstract: Semiconductor wafer fabrication is a complicated and time-consuming production process in the semiconductor manufacturing industry. It is very important for the manufacturer to come up with production plans that can most efficiently utilise the manufacturing equipment and fulfil customer orders placed in a planning horizon. Because of the complexity of the manufacturing processes, it is necessary to use high-fidelity discrete-event simulations to provide accurate estimates of delivery lead time for any given production plan. However, high-fidelity simulations are time-consuming, and thus decision-makers may only evaluate a small number of production plans once customer orders are received. In this paper, we propose the use of a multi-fidelity simulation optimisation approach to efficiently evaluate and select the best production plan from a large set of alternative plans under consideration. We develop an open queue approximation model for a wafer fabrication system and then use the low-fidelity estimates of lead times obtained from the approximation model in a recently developed multi-fidelity simulation optimisation method. Simulation experiment results show that the multi-fidelity approach significantly improves the computational efficiency of simulation-based production planning.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2019.1683252 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:58:y:2020:i:21:p:6585-6600

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2019.1683252

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().