An adaptive run-to-run optimizing controller for linear and nonlinear semiconductor processes

This paper presents a new run-to-run (R2R) multiple-input-multiple-output controller for semiconductor manufacturing processes. The controller, termed optimizing adaptive quality controller (OAQC), can act both as an optimizer-in case equipment models are not available-or as a controller for given models. The main components of the OAQC are shown and a study of its performance is presented. The controller allows one to specify input and output constraints and weights, and input resolutions. A multivariate control chart can be applied either as a deadband on the controller or simply to provide out of control alarms. Experimental designs can be utilized for on-line (recursive) model identification in the optimization phase. For testing purposes, two chemical mechanical planarization processes were simulated based on real equipment models. It is shown that the OAQC allows one to keep adequate control even if the input-output transfer function is severely nonlinear. Software implementation including the integration of the OAQC with the University of Michigan's Generic Cell Controller (GCC) is briefly discussed.

[1]  Costas J. Spanos,et al.  A control system for photolithographic sequences , 1996 .

[2]  Duane S. Boning,et al.  DOE/Opt: a system for design of experiments, response surface modeling, and optimization using process and device simulation , 1994 .

[3]  Armann Ingolfsson,et al.  Run by run process control: combining SPC and feedback control , 1995 .

[4]  Lennart Ljung,et al.  Theory and Practice of Recursive Identification , 1983 .

[5]  Jinn-Yi Yeh,et al.  A comparative analysis of run-to-run control algorithms in the semiconductor manufacturing industry , 1996, IEEE/SEMI 1996 Advanced Semiconductor Manufacturing Conference and Workshop. Theme-Innovative Approaches to Growth in the Semiconductor Industry. ASMC 96 Proceedings.

[6]  James Moyne,et al.  A process-independent run-to-run controller and its application to chemical-mechanical planarization , 1995, Proceedings of SEMI Advanced Semiconductor Manufacturing Conference and Workshop.

[7]  P. Khargonekar,et al.  Integrated real-time and run-to-run control of etch depth in reactive ion etching , 1997 .

[8]  G. Nanz,et al.  Modeling of chemical-mechanical polishing: a review , 1995 .

[9]  P. K. Mozumder,et al.  Statistical feedback control of a plasma etch process , 1994 .

[10]  S. W. Butler,et al.  Supervisory run-to-run control of polysilicon gate etch using in situ ellipsometry , 1994 .

[11]  B. Erik Ydstie,et al.  Adaptive extremum control using approximate process models , 1989 .

[12]  M. Zarrop,et al.  Convergence of a multi-input adaptive extremum controller , 1991 .

[13]  Charles W. Champ,et al.  A multivariate exponentially weighted moving average control chart , 1992 .

[14]  Duane S. Boning,et al.  Practical issues in run by run process control , 1995, Proceedings of SEMI Advanced Semiconductor Manufacturing Conference and Workshop.

[15]  S. Saxena,et al.  A monitor wafer based controller for semiconductor processes , 1994 .

[16]  Evanghelos Zafiriou,et al.  An approach to run-to-run control for rapid thermal processing , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[17]  Arnon M. Hurwitz,et al.  Run-to-Run Process Control: Literature Review and Extensions , 1997 .

[18]  Enrique Del Castillo,et al.  A multivariate self-tuning controller for run-to-run process control under shift and trend disturbances , 1996 .