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Dynamic scheduling of photolithography process based on Kohonen neural network

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Abstract

Semiconductor enterprises should continue to provide more capacity to meet the demands of the highly competitive semiconductor markets. Photolithography is usually the bottleneck process with the most expensive equipment in a semiconductor wafer fabrication system. Usually, photolithography area controls the performance of whole semiconductor manufacturing system. To improve the performances of the photolithography area with dynamic dispatching combination rules, a dynamic scheduling method based on Kohonen neural network (KNN) was proposed in this paper. A dynamic scheduling framework is also proposed. The method has been integrated into the dynamic scheduling framework. A KNN-based sample learning algorithm for selecting best combination rules is presented. Finally, the results of simulation experiments indicate that the proposed method is effective and feasible in real-time scheduling of semiconductor fabrication system under both closed-loop release policy and open-loop release policy.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (No. 71071115, No. 61273035).

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Authors and Affiliations

  1. School of Mechanical Engineering, Tongji University, Shanghai, 201804, People’s Republic of China

    Bing-hai Zhou

  2. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, People’s Republic of China

    Xin Li & Richard Y. K. Fung

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  1. Bing-hai Zhou
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  2. Xin Li
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  3. Richard Y. K. Fung
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Correspondence to Bing-hai Zhou.

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Zhou, Bh., Li, X. & Fung, R.Y.K. Dynamic scheduling of photolithography process based on Kohonen neural network. J Intell Manuf 26, 73–85 (2015). https://doi.org/10.1007/s10845-013-0763-9

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  • DOI: https://doi.org/10.1007/s10845-013-0763-9

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