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Discrete Wavelet Transform and Radial Basis Neural Network for Semiconductor Wet-Etching Fabrication Flow-Rate Analysis

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1 Author(s)
Wen-Ren Yang ; Dept. of Electr. Eng., Nat. Changhua Univ. of Educ., Changhua, Taiwan

This paper presents research that uses discrete wavelet transform (DWT) and radial basis neural network for automatic classification. The flow rate of a wet-etching fabrication facility for a single wafer can be analyzed automatically. The electrical signal of a flow meter is collected and decomposed by means of DWT. The signal power of the coefficients processed by the DWT is fed into the radial basis neural network for initial classification. A digital filter for post signal processing and a user-defined threshold value are applied; calculations for successful identification rate take place at the final step. The research results are applicable to automatic identification functions for in situ fabrication monitoring.

Published in:

Instrumentation and Measurement, IEEE Transactions on  (Volume:61 ,  Issue: 4 )

Date of Publication:

April 2012

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