Abstract
Using a time series model, we constructed a disturbance model for the aluminum sputter deposition process, and derived an autoregressive integrated moving average and recursive least-squares (ARIMA-RLS) controller based on this new disturbance model. Experimental results revealed that the ARI(3,1) model appropriately characterized the dynamic behavior of the disturbance for this process. The ARIMA-RLS controller, which includes information on process noise, is able to automatically regulate the model coefficients as the target is replaced or degrades. In this paper, the d-EWMA controller, the age-based d-EWMA controller, and the ARIMA-RLS controller were applied to aluminum sputter deposition processes in order to predict deposition rates and compare their performances. Application of the ARIMA-RLS controller is proven herein to significantly improve the estimation accuracy of the aluminum sputter deposition process, regardless of whether or not deposition rates are measured for each run.
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References
Spanos CJ, Guo HF, Miller A, Joanne LP (1992) Real-time statistical process control using tool data. IEEE Trans Semicond Manuf 5:308–318
Sachs E, Guo RS, Ha S, Hu A (1991) Process control system for VLSI fabrication. IEEE Trans Semicond Manuf 4:134–144
Sachs E, Hu A, Ingolfsson A (1995) Run by run process control: combining SPC and feedback control. IEEE Trans Semicond Manuf 8:26–43
Smith TH, Boing DS (1997) A self-tuning EWMA controller utilizing artificial neural network function approximation techniques. IEEE Trans Compon Packag Manuf Technol C* 20:121–132
Guo RS, Chen JJ, Chen A, Lu SS (1999) A self-tuning run-by-run process controller for processes subject to random disturbances. J Chin Inst Eng 22:627–638
Tseng ST, Yeh B, Tsung F, Chan YY (2003) A study of variable EWMA controller. IEEE Trans Semicond Manuf 16:633–643
Hsu CC, Su CT (2004) A neural network-based adaptive algorithm on the single EWMA controller. Int J Adv Manuf Technol 23:586–593
Butler SW, Stefani JA (1994) Supervisory run-to-run control of polysilicon gate etch using in situ ellipsometry. IEEE Trans Semicond Manuf 7:193–201
Sattler L, Hecker PE (1998) Reducing cost and increasing throughput using model-based process control on sputtering system. IEEE/CPMT international electronics manufacturing technology symposium, Austin, USA, pp 409–414
Guo RS, Chen A, Chen JJ (2000) Run-to-run control schemes for CMP process subject to deterministic drifts. Semiconductor manufacturing technology workshop, Hsinchu, Taiwan, ROC, pp 251–258
Chen A, Guo RS (2001) Age-based double EWMA controller and its application to CMP process. IEEE Trans Semicond Manuf 14:11–19
Tseng ST, Song W, Chang YC (2005) An initial intercept iteratively adjust (IIIA) controller: an enhanced double EWMA feedback control scheme. IEEE Trans Semicond Manuf 18:448–457
Tseng ST, Chou RJ, Lee SP (2002) A study on a multivariate EWMA controller. IIE Trans 34:541–549
Del Castillo E, Rajagopal R (2002) A multivariate double EWMA process adjustment scheme for drifting process. IIE Trans 34:1055–1068
Yang L, Sheu SH (2006) Integrating multivariate engineering process control and multivariate statistical control. Int J Adv Manuf Technol 29:129–136
Wang GJ, Yu CH (2006) Developing a neural network-based run-to-run process controller for chemical-mechanical planarization. Int J Adv Manuf Technol 28:899–908
Smith TH, Boning DS, Stefani J, Butler SW (1998) Run by run advanced process control of metal sputter deposition. IEEE Trans Semicond Manuf 11:276–284
Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis forecasting and control. Prentice-Hall, Englewood Cliffs, NJ
Fung EHK, Chung APL (1999) Using ARMA models to forecast workpiece roundness error in a turning operation. Appl Math Model 23:567–585
Young P (1984) Recursive estimation and time-series analysis. Springer, Berlin Heidelberg New York
Liung L, Soderstrom T (1986) Theory and practice of recursive identification. MIT Press, Cambridge, MA
Graupe D (1989) Time series analysis, identification and adaptive filtering. Krieger, Malabar, FL
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Chen, JH., Kuo, TW. & Chen, TC. Advanced process control of metal sputter deposition using a time series analysis. Int J Adv Manuf Technol 36, 501–509 (2008). https://doi.org/10.1007/s00170-006-0864-0
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DOI: https://doi.org/10.1007/s00170-006-0864-0