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Multivariable Optimal Learning Control of Wafer Temperatures in a Commercial RTP Equipment
Author(s) -
Cho Moon K.,
Joo Sang R.,
Won Seung H.,
Lee Kwang S.
Publication year - 2005
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450830227
Subject(s) - multivariable calculus , materials science , temperature control , wafer , model predictive control , computer science , optoelectronics , mechanical engineering , engineering , artificial intelligence , control engineering , control (management)
Abstract A multivariable optimal iterative learning control technique called BMPC (Batch Model Predictive Control) has been implemented and evaluated in a commercial RTP (Rapid Thermal Processing) system fabricating 200 mm silicon wafers. The wafer temperature was controlled at multiple points along the radial direction by manipulating multiple tungsten‐halogen lamp groups. The study has addressed the following two issues: feasibility of BMPC in a commercial RTP equipment and enhancement of temperature uniformity using redundant inputs. As a consequence, satisfactory tracking performance could be realized with BMPC with reduced efforts for design and implementation of the controller by the standardized identification and tuning procedure. Redundant inputs whose number is larger than that of the temperature measurements was attempted to relieve the directionality of the system. Experimental tests revealed that the approach can provide us with improved temperature uniformity as well as tracking performance.