Simulation of Rapid Thermal Process Modelling，Parameter Identification，and Temperature Control

Abstract

An integrated model is given for the components that make up a rapid thermal processing （ RTP ） system ． Thess 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 model ） 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 temporally 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． #96B4552