Abstract
Chamber scheduling in etching tools is an important but difficult task in integrated circuit manufacturing. In order to effectively solve such combinatorial optimization problems in etching tools, this paper presents a novel chamber scheduling approach on the base of Adaptive Artificial Neural Networks (ANNs). Feed forward, multi-layered neural network meta-models were trained through the back-error-propagation (BEP) learning algorithm to provide a versatile job-shop scheduling analysis framework. At the same time, an adaptive selection mechanism has been extended into ANN. By testing the practical data set, the method is able to provide near-optimal solutions for practical chamber scheduling problems, and the results are superior to those generated by what have been reported in the neural network scheduling literature.
This work was jointly supported by the National Nature Science Foundation for Youth Fund (Grant No: 60405011), the China Postdoctoral Foundation for China Postdoctoral Science Fund (Grant No: 20040350078) and the National 863 Project of China.
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References
LAM Research Company: LAM 9400 Handbook, California (2001)
Fonseca, D.J., Navaresse, D.: Artificial Neural Networks for Job Shop Simulation. Advanced Engineering Informatics 16, 241–246 (2002)
Dempster, M., Lenstra, J., Kan, R.: Deterministic and Stochastic Scheduling: Introduction. In: Proceedings of the NATO Advanced Study and Research Institute on Theoretical Approaches to Scheduling Problems, vol. 1, pp. 3–14 (1981)
Chenga, R., Genb, M., Tsujimura, Y.: A Tutorial Survey of Job-shop Scheduling Problems Using Genetic Algorithms, Part II: Hybrid Genetic Search Strategies. Computers & Industrial Engineering 36, 343–364 (1999)
Jones, A., Rabelo, L.: Survey of Job Shop Scheduling Techniques. Gaithersburg, MD: NISTR, National Institute of Standards and Technology, Gaithersburg (1998)
Shengxiang, Y., Dingwei, W.: A New Adaptive Neural Network and Heuristics Hybrid Approach for Job-shop Scheduling. Computers & Operations Research 28, 955–971 (2001)
Dubois, D., Fargier, H., Prade, H.: Fuzzy Constraints in Job Shop Scheduling. Journal of Intelligent Manufacturing 6, 215–234 (1995)
Montazeri, H.: Analysis of Scheduling Rules for an FMS. International Journal of Production Research 28, 785–802 (1990)
Garey, M.R., Johnson, D.S., Sethi, R.: The Complexity of Flow Shop and Job-shop Scheduling. Math. Opl. Res. 1, 117–129 (1976)
French, S.: Sequencing and Scheduling: An Introduction to the Mathematics of the Jobshop. Wiley, New York (1982)
Foo, S.Y., Takefuji, Y.: Integer Linear Programming Neural Networks for Job-shop Scheduling. In: Proceedings of IEEE IJCNN, San Diego, vol. 2, pp. 341–348 (1988)
Foo, S.Y., Takefuji, Y., Szu, H.: Job-shop Scheduling Based on Modeled Tank-Hopfield Linear Programming Networks. Engineering Application and Artificial Intelligent 7, 321–327 (1994)
Zhou, D.N., Charkassky, V., Baldwin, T.R., Hong, D.W.: Scaling Neural Network for Job- Shop Scheduling. In: Proceedings of IEEE International Joint Conference on Neural Networks, New York, vol. 3, pp. 889–894 (1989)
Willems, T.M., Brandts, L.: Implementing Heuristics as an Optimization Criterion in Neural Networks for Job-shop Scheduling. Journal of Intelligent Manufacturing 6, 377–387 (1995)
Yang, S., Wang, D.: Constraint Satisfaction Adaptive Neural Network and Heuristics Combined Approaches for Generalized Job-shop Scheduling. IEEE Transactions on Neural Networks 11, 474–486 (2000)
Zhang, C.S., Yan, P.F.: Neural Network Method of Solving Job-shop Scheduling Problem. Acta Automatic Sinica 21, 706–712 (1995)
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Xu, H., Jia, P., Zhang, X. (2005). A Novel Chamber Scheduling Method in Etching Tools Using Adaptive Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_144
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DOI: https://doi.org/10.1007/11427469_144
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