Emissivity-free radiation thermometry based on multivariate analysis of spectral radiance applied to steel making process

Author(s):  
Takahiko Oshige ◽  
Takahiro Koshihara ◽  
Shino Hirota ◽  
Toshiki Isobe ◽  
Mitsutoshi Kemmochi
2000 ◽  
Author(s):  
Y. H. Zhou ◽  
Y. J. Shen ◽  
Z. M. Zhang ◽  
B. K. Tsai ◽  
D. P. DeWitt

Abstract This work employs a Monte Carlo method to study the radiative process in a rapid thermal processing (RTP) furnace. A “true” effective emissivity, accounting for the directional optical properties, is defined and predicted in order to determine the wafer temperature from the measured spectral radiance temperature using light-pipe radiation thermometry. The true effective emissivity is the same as the hemispherical effective emissivity for diffuse wafers, in which case the Monte Carlo model gives the same results as the net-radiation method. Deviations exist between the hemispherical effective emissivity and the true effective emissivity for specular wafers because the effective emissivity is directional dependent. This research will help reduce the uncertainty in the temperature measurement for RTP furnaces to meet the future requirements for integrated circuit manufacturing.


1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.


2005 ◽  
Vol 173 (4S) ◽  
pp. 303-303
Author(s):  
Diana Wiessner ◽  
Rainer J. Litz ◽  
Axel R. Heller ◽  
Mitko Georgiev ◽  
Oliver W. Hakenberg ◽  
...  

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