Abstract
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.
| Original language | English |
|---|---|
| Article number | 6125246 |
| Pages (from-to) | 865-875 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Instrumentation and Measurement |
| Volume | 61 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2012 Apr 1 |
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All Science Journal Classification (ASJC) codes
- Instrumentation
- Electrical and Electronic Engineering
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Discrete wavelet transform and radial basis neural network for semiconductor wet-etching fabrication flow-rate analysis. / Yang, Wen Ren.
In: IEEE Transactions on Instrumentation and Measurement, Vol. 61, No. 4, 6125246, 01.04.2012, p. 865-875.Research output: Contribution to journal › Article
TY - JOUR
T1 - Discrete wavelet transform and radial basis neural network for semiconductor wet-etching fabrication flow-rate analysis
AU - Yang, Wen Ren
PY - 2012/4/1
Y1 - 2012/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84858337554&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84858337554&partnerID=8YFLogxK
U2 - 10.1109/TIM.2011.2179824
DO - 10.1109/TIM.2011.2179824
M3 - Article
AN - SCOPUS:84858337554
VL - 61
SP - 865
EP - 875
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
SN - 0018-9456
IS - 4
M1 - 6125246
ER -