Temperature control in a rapid thermal processor

Dilhac, Jean‐Marie; Ganibal, C.; Bordeneuve, J.; Nolhier, Nicolas

doi:10.1109/16.108231

Cited by 26 publications

(6 citation statements)

References 6 publications

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“…For such systems, therefore, traditional model-based MIMO (Multi-Input Multi-Output) control techniques may show limited performance because of their strong model dependency. Such a problem is manifest from the previous studies (Breedijk et al, 1993;Cho et al, 1997;Dilhac et al, 1992;Gyugyi et al, 1992;Schaper et al, 1994;Stuber et al, 1994;Theodoropoulou., 1999).…”

Section: Multivariable Optimal Learning Control Of Multivariable Optimentioning

confidence: 95%

Multivariable Optimal Learning Control of Wafer Temperatures in a Commercial RTP Equipment

Cho¹,

Joo²,

Won

et al. 2008

Can. J. Chem. Eng.

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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.

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Section: Multivariable Optimal Learning Control Of Multivariable Optimentioning

confidence: 95%

Multivariable Optimal Learning Control of Wafer Temperatures in a Commercial RTP Equipment

Cho¹,

Joo²,

Won

et al. 2008

Can. J. Chem. Eng.

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“…Examining the radiative heat transfer terms leads us to consider that an RTP system would be better represented by a linear dynamic model that relates uðtÞ to T 4 w ðtÞ, rather to T w ðtÞ as previously done (Breedijk et al, 1993;Dilhac et al, 1992;Schaper et al, 1994;Stuber et al, 1994;Yang, Lee, & Ahn, 2003). Here T w represents a vector containing the wafer temperatures at the eight locations.…”

Section: Article In Pressmentioning

confidence: 98%

“…All these factors combine to make reliable modeling of the system very difficult (Cho & Gyugyi, 1997). In the presence of significant model errors, traditional feedback control techniques, though successfully applied in some commercial RTP equipments (Peuse, Miner, Yam, & Elia, 1998), may not provide satisfactory temperature tracking performance, as evidenced in some of the previous studies (Breedijk, Edgar, & Trachtenberg, 1993;Dilhac, Ganibal, Bordeneuve, & Nolhier, 1992;Schaper, Moslehi, Sarawat, & Kailath, 1994;Stuber, Trachtenberg, & Edgar, 1994).…”

Section: Introductionmentioning

confidence: 93%

Iterative learning control applied to batch processes: An overview

Lee

2007

Control Engineering Practice

256

106

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With the recent emphasis on batch processing by emerging industries like the microelectronics and biotechnology, the interest in batch process control has been renewed. This paper gives an overview of the iterative learning control (ILC) technique, which can be used to improve tracking control performance in batch processes. The fundamental concepts and review of the various ILC algorithms are presented, with a particular focus on a model-based algorithm called Q-ILC and an application involving a rapid thermal processing (RTP) system. The study indicates that one can solve a seemingly very difficult multivariable nonlinear tracking problem with relative ease by intelligently combining the ILC technique with basic process insights and standard system identification techniques. Some related techniques in the literature are brought forth with the hope of unifying them. We aslo suggest some remaining challenges. r

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“…For such a surface, the radiosity (total radiant emission per unit area) is represented by (2) where and represent the black-body emissive power and the irradiation (incident radiation energy per unit area), respectively. From this relation, the net radiant heat flow from a surface of area is (3) The net interchange between the two surfaces with area and is represented as (4) where is the view factor from to .…”

Section: A Basic Radiation Equationsmentioning

confidence: 99%

Semi-Empirical Model-Based Multivariable Iterative Learning Control of an RTP System

Cho¹,

Lee

Joo

et al. 2005

IEEE Trans. Semicond. Manufact.

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Comprehensive study on control system design for a rapid thermal processing (RTP) equipment has been conducted with the purpose to obtain maximum temperature uniformity across the wafer surface, while precisely tracking a given reference trajectory. The study covers from model development, identification, optimum multivariable iterative learning control (ILC), to reduced-order controller design. The highlight of the study is the ILC technique on the basis of a semi-empirical dynamic radiation model named as 4 -model. It was shown that the 4 -model-based ILC technique can remarkably improve the performance of RTP control compared with the ordinary linear model-based ILC. In addition, reduced-order control methods and the associated optimum sensor location have been addressed. The proposed techniques have been evaluated in an RTP equipment fabricating 8-in wafers.Index Terms-Iterative learning control (ILC), linear quadratic Gaussian (LQG), rapid thermal processing (RTP) control, RTP modeling.

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Temperature control in a rapid thermal processor

Cited by 26 publications

References 6 publications

Multivariable Optimal Learning Control of Wafer Temperatures in a Commercial RTP Equipment

Multivariable Optimal Learning Control of Wafer Temperatures in a Commercial RTP Equipment

Iterative learning control applied to batch processes: An overview

Semi-Empirical Model-Based Multivariable Iterative Learning Control of an RTP System

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