Discrete wavelet transform and radial basis neural network for semiconductor wet-etching fabrication flow-rate analysis

Wen Ren Yang

doi:10.1109/TIM.2011.2179824

Discrete wavelet transform and radial basis neural network for semiconductor wet-etching fabrication flow-rate analysis

Wen Ren Yang

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

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	https://doi.org/10.1109/TIM.2011.2179824
Publication status	Published - 2012 Apr

All Science Journal Classification (ASJC) codes

Instrumentation
Electrical and Electronic Engineering

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10.1109/TIM.2011.2179824

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title = "Discrete wavelet transform and radial basis neural network for semiconductor wet-etching fabrication flow-rate analysis",

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.",

author = "Yang, {Wen Ren}",

note = "Funding Information: Manuscript received June 20, 2011; revised September 9, 2011; accepted October 13, 2011. Date of publication January 6, 2012; date of current version March 9, 2012. This work was supported in part by the National Science Council, Taiwan, under Grant 100-2221-E-018-001. The Associate Editor coordinating the review process for this paper was Dr. Kurt Barbe.",

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