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研究生:黃睿政
研究生(外文):Huang, Jui-Cheng
論文名稱:快速熱處理之模擬,參數判別和溫度控制
論文名稱(外文):Simulation of Rapid Thermal Process Modelling,Parametres Identification and Temperature Control
指導教授:林進燈林進燈引用關係
指導教授(外文):Chin-Teng Lin
學位類別:碩士
校院名稱:國立交通大學
系所名稱:控制工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1996
畢業學年度:84
語文別:中文
論文頁數:119
中文關鍵詞:快速熱處理模型基礎系統判別
外文關鍵詞:RTPmodel-basedsystem identificationNeural NetworkNeural FuzzyInverse control
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快速熱處理是一種用在半導體製程中熱處理的新型的機器。其具有快速
的加熱速度及極低的熱預算,然而,有一些瓶頸限制了此項技術應用在半
導體工業上,其中溫度的控制及均溫皆是需要突破的關鍵技術。我們採用
了模型基礎的控制方法去做快速熱處理的溫度控制,先用一些系統判別的
方法,再利用判別出來的模型去做控制器。然而有些情況,系統判別出來
的模型並不精確,這時我們採用類神經網路的方式,去做控制,並利用多
輸入多輸出的方式,做均溫的控制。

An integrated model is given for the components that make up a
rapid thermal processing (RTP) system. These components are the
lamp power dynamics, ray-tracing model, and the wafer thermal
dynamic model. The models for the components are integrated in
a numerical code to give a computer simulation of the complete
RTP system. The simulation can be used to investigate the
interaction of the furnace, lamp contour, and the control
system. Of the various techniques for controlling the
temperature of the RTP system,model-based control has the
greatest potential for attaining the best performance in the
field of conventional control, when the model is accurate. In
this thesis, some identification methods are introduced to help
obtain more accurate models from measured input-output data. At
first, identification techniques are presented to estimate the
parameters (time constant and gain) of step response. Then show
how to use the input-output measurements to obtain a black-box
model (autoregressive exogenous mo del) of the RTP system, which
turns out to have better prediction capability. The identified
models are useful not only to control law design but also to
analysis of system characteristics.We apply the tracking system
technique that is known in the digital control literature for
the model-based control, and propose a controller-switching rule
throughout the nonlinear operating region. To overcome ill
performance of the tracking system due to the inaccuracy of the
identified model, the neural fuzzy network for inverse control
is proposed. The key advantage of neural fuzzy approach over
traditional ones lies on that the approach doesn't require a
mathematical description of the system while controlling. The
neural fuzzy network that we use is the Self-cOnstructing
Neural Fuzzy Inference Network (SONFIN) proposed by Junag and
Lin. Temperature control involves maintaining a spatially
uniform temperature distribution across the wafer while
tracking a temp orally varying temperature trajectory such as a
rapid ramp to processing temperatures. The combination of lamp
added RTP system and the MIMO SONFIN is shown to be able to
track a temporally varying temperature trajectory and maintain
the uniformity of the spatial temperature distribution
simultaneously.


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