Dynamic Scheduling Approaches to Wafer Test Scheduling with Unpredictable Error
Tsubasa Matsuo*, Masahiro Inuiguchi*, and Kenichiro Masunaga**
*Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka 560-8531, Japan
**Renesas Electronics Co., 2-6-2 Otemachi, Chiyoda-ku, Tokyo 100-0004, Janapn
Scheduling of semiconductor wafer testing processes can be seen as a resource constraint project scheduling problem (RCPSP). However, it includes uncertainties caused by human factors, wafer errors and so on. Because some uncertainties are not simply quantitative, range estimation of the parameters would not be very useful. Considering such uncertainties, finding a good situation-dependent dispatching rule would be more suitable than solving the RCPSP under uncertainties. In this paper we apply the Pitts approach, one of the genetic algorithms, to the situation-dependent dispatching rule acquisition. We compare the obtained rule with the simple dispatching rules and examine the effectiveness and usefulness of the obtained rule in the problems with unpredictable wafer errors.
-  D. Ouelhadj and S. Petrovic, “A Survey of Dynamic Scheduling in Manufacturing Systems,” J. Sched., Vol.12, pp. 417-431, 2009.
-  I. M. Ovacik and R. Uzsoy, “Decomposition Methods for Scheduling Semiconductor Testing Facilities,” The Int. J. of Flexible Manufacturing Systems, Vol.8, pp. 357-388, 1996.
-  Y. Shen and R. C. Leachman, “Stochastic Wafer Fabrication Scheduling,” IEEE Tran. Semiconductor Manufacturing. Vol.16, No.1, pp. 2-14, 2003.
-  J.-Z. Wu and C.-F. Chien, “Modeling Semiconductor Testing Job Scheduling and Dynamic Testing Machine Configuration,” Expert Systems with Applications, Vol.35, pp. 485-496, 2008.
-  J. H. Holland. “Escaping Brittleness: The Possibilities of General Purpose Learning Algorithms Applied to Parallel Rule-based System,” Machine Learning: An Artificial Intelligence Approach, Vol.2, Morgan Kaufmann, Los Altos, pp. 593-623, 1986.
-  S. F. Smith, “Flexible learning of problem solving heuristics through adaptive search,” Proc. 8th Int. Joint Conf. on Artificial Intelligence, Vol.1, pp. 422-425, 1983.
-  K. Sakakibara, “Research about Scheduling Rule Acquisition Based on Genetic Based Machine Learning,” Doctor Thesis, Graduate School of Science and Technology, Kobe University, 2004.
-  Z. Michalewicz, “PPGenetic Algorithms + Data Structures = Evolution Programs,” Third, Revised and Extended Edition, Springer, Berlin, 1998.