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Developing a neural network-based run-to-run process controller for chemical-mechanical planarization

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Abstract

A new neural network-based run-to-run process control system (NNRtRC) is proposed in this article. The key characteristic of this NNRtRC is that the linear and stationary process estimator and controller in the exponentially weighted moving average (EWMA) run-to-run control scheme are replaced by two multilayer feed-forward neural networks. An efficient learning algorithm inspired by the sliding mode control law is suggested for the neural network-based run-to-run controller. Computer simulations illustrate that the proposed NNRtRC performs better than the EWMA approach in terms of draft suppression and adaptation to environmental change. Experimental results show that the NNRtRC can precisely trace the desired target of material removal rate (MRR) and keep the within wafer nonuniformity (WIWNU) in an acceptable range.

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References

  1. Sachs E, Guo R, Ha S, Hu A (1991) Process control system for VLSI fabrication. IEEE Trans Semiconductor Manuf 4:134–144

    Article  Google Scholar 

  2. Sachs E, Hu A, Ingolfsson A (1995) Run by run process control: combining SPC and feedback control. IEEE Trans Semiconductor Manuf 8:26–43

    Article  Google Scholar 

  3. Khan K, Chemali CE, Moyne J, Chapple-Sokol J, Nadeau R, Smith P, Colt J, Parikh T (1999) Yield improvement at the contact process through run-to-run control. In: Proceedings of the IEEE/CPMT Intermational Electronics Manufacturing Technology Symposium, pp 258–263

  4. Moyne JR, Telfeyan R, Hurwitz A, Taylor J (1995) A processindependent run-to-run controller and its application to chemicalmechanical planarization. In: Proceedings of the IEEE/SEMI Advanced Semiconductor Manufacturing Conference, pp 194–200

  5. Hu A, Zhang X, Sachs E, Renteln P (1993) Application of run by run controller to the chemical-mechanical planarization process, part I. In: Proceedings of the IEEE/CPMT International Electronics Manufacturing Technology Symposium, pp 235–240

  6. Hu A, Du H, Wong S, Renteln P, Sachs E (1994) Application of run by run controller to the chemical-mechanical planarization process, part II. In: Proceedings of the IEEE/CPMT International Electronics Manufacturing Technology Symposium, pp 371–378

  7. Boning DS, Moyne WP, Smith TH, Moyne J, Telfeyan R, Hurwitz A, Shellman S, Taylor J (1996) Run-by-run control of chemicalmechanical polishing. IEEE Trans Compon Packag Manuf Technol Part C 19(4):307–314

    Article  Google Scholar 

  8. Ingolfsson A, Sachs E (1993) Stability and sensitivity of an EWMA controller. J Qual Technol 25(4):271–287

    Google Scholar 

  9. Smith TH, Boning DS (1997) A self-tuning EWMA controller utilizing artificial neural network function approximation techniques. IEEE Trans Compon Packag Manuf Technol, Part C 20(2):121–132

    Article  Google Scholar 

  10. Patel NS, Jenkins ST (2000) Adaptive optimization of run-to-run controller: the EWMA example. IEEE Trans Semiconductor Eng 13(1):97–107

    Article  Google Scholar 

  11. Guo RS, Huang LS, Chen A, Chen JJ (1997) A cost-effective methodology for a run-by-run EWMA controller. In: Proceeding of the 1997 International Symposium on Semiconductor Manufacturing, pp 61–64

  12. Hamby ES, Kabamba PT, Khargonekar PP (1998) A probabilistic approach to run-to-run control. IEEE Trans Semiconductor Manuf 11(4):654–669

    Article  Google Scholar 

  13. Edgar TF, Campbell WJ, Bode C (1999) Model-based control in microelectronics manufacturing. In: IEEE Proceedings of the 38th Conference on Decision and Control, Phoenix, AZ, pp 4185–4191

  14. Chen F (1990) Back-propagation neural networks for non-linear self-tuning adaptive control. IEEE Contr Syst Mag 10:44–48

    Article  Google Scholar 

  15. Fukuda T, Shibata T (1992) Theory and application of neural networks for industrial control systems. IEEE Trans Ind Electr 39(6):3–20

    Google Scholar 

  16. Sastry PS, Santharam G, Unnikrishnan KP (1994) Memory neural networks for identification and control of dynamical systems. IEEE Trans Neural Netw 5(2):306–319

    Article  Google Scholar 

  17. Wang GJ, Chen JL, Hwang JY (2001) A new optimization strategy for chemical-mechanical polishing process. JSME Int J Series C 44(2):534–543

    Article  Google Scholar 

  18. Wang GJ, Tsai JC, Tzeng PC, Cheng TC (1998) Neural-Taguchi method for robust design analysis. J Chin Soc Mech Eng 19(2):223–230

    Google Scholar 

  19. Taguchi G, Wu YI (1979) Introduction to off-line quality control. Quality Control Association, Nagayo, Japan

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Chung-Hsing University, 402, Taichung, Taiwan

    Gou-Jen Wang & Cheng-Hsiong Yu

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  1. Gou-Jen Wang
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  2. Cheng-Hsiong Yu
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Correspondence to Gou-Jen Wang.

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Wang, GJ., Yu, CH. Developing a neural network-based run-to-run process controller for chemical-mechanical planarization. Int J Adv Manuf Technol 28, 899–908 (2006). https://doi.org/10.1007/s00170-004-2451-6

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  • DOI: https://doi.org/10.1007/s00170-004-2451-6

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