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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2282/1155
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| Title: | A Sensor Fusion Algorithm for Filtering Pyrometer Measurement Noise in the Czochralski Crystallization Process |
| Authors: | Komperød, Magnus
Bones, John Atle
Lie, Bernt |
| Issue Date: | 2011 |
| Publishers version: | http://dx.doi.org/10.4173/mic.2011.1.2 |
| Abstract: | The Czochralski (CZ) crystallization process is used to produce monocrystalline silicon for solar cell
wafers and electronics. Tight temperature control of the molten silicon is most important for achieving
high crystal quality. SINTEF Materials and Chemistry operates a CZ process. During one CZ batch, two
pyrometers were used for temperature measurement. The silicon pyrometer measures the temperature of
the molten silicon. This pyrometer is assumed to be accurate, but has much high-frequency measurement
noise. The graphite pyrometer measures the temperature of a graphite material. This pyrometer has little
measurement noise. There is quite a good correlation between the two pyrometer measurements. This
paper presents a sensor fusion algorithm that merges the two pyrometer signals for producing a temperature
estimate with little measurement noise, while having signi cantly less phase lag than traditional lowpass-
ltering of the silicon pyrometer. The algorithm consists of two sub-algorithms: (i) A dynamic model is
used to estimate the silicon temperature based on the graphite pyrometer, and (ii) a lowpass lter and a
highpass lter designed as complementary lters. The complementary lters are used to lowpass- lter the
silicon pyrometer, highpass- lter the dynamic model output, and merge these ltered signals. Hence, the
lowpass lter attenuates noise from the silicon pyrometer, while the graphite pyrometer and the dynamic
model estimate those frequency components of the silicon temperature that are lost when lowpass- ltering
the silicon pyrometer. The algorithm works well within a limited temperature range. To handle a larger
temperature range, more research must be done to understand the process' nonlinear dynamics, and build
this into the dynamic model. |
| Keywords: | Complementary filters
Czochralski crystallization process
Measurement noise filtering
Pyrometer temperature measurement
Sensor fusion algorithm |
| Document type: | Journal article |
| Appears in Collections: | Institutt for elektro, IT og kybernetikk
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