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Journal of Information Science and Engineering, Vol. 24 No. 2, pp. 379-391 (March 2008)

Load Balancing Among Photolithography Machines in the Semiconductor Manufacturing System*

Arthur M. D. Shr1,2, Alan Liu1 and Peter P. Chen2
1Department of Electrical Engineering and Center for Telecommunication Research
National Chung Cheng University
Chiayi, 621 Taiwan
E-mail: arthurshr@gmail.com; aliu@ee.ccu.edu.tw
2Department of Computer Science
Louisiana State University
Baton Rouge, LA 70803, U.S.A.
E-mail: {mshr1; pchen}@lsu.edu

We propose a Load Balancing (LB) scheduling approach to tackle the load balancing issue in the semiconductor manufacturing system. This issue is derived from the dedicated photolithography machine constraint. The constraint of having a dedicated machine for the photolithography process in semiconductor manufacturing is one of the new challenges introduced in photolithography machinery due to natural bias. To prevent the impact of natural bias, the wafer lots passing through each photolithography process have to be processed on the same machine. However, the previous research for the semiconductor manufacturing production has not addressed the load balancing issue and dedicated photolithography machine constraint. In this paper, along with providing the LB approach to the issue, we also present a novel model, Resource Schedule and Execution Matrix (RSEM) V the representation and manipulation methods for the task process patterns. The advantage of the proposed approach is to easily schedule the wafer lots by using a simple two-dimensional matrix. We also present the simulation results to validate our approach.

Keywords: dedicated photolithography machine constraint, load balancing, resource schedule and execution matrix, semiconductor manufacturing, photolithography

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Received January 9, 2006; revised September 21, 2006; accepted January 23, 2007.
Communicated by Suh-Yin Lee.
*This research was partially supported by the U.S. National Science Foundation grant No. IIS-0326387 and U.S. AFOSR grant No. FA9550-05-1-0454. This research was also supported in part by the Ministry of Economic Affairs under the grant No. 96-EC-17-A-02-S1-029 and the National Science Council under the grants No. 96-2752-E-008-002-PAE and 95-2221-E-194-009.