Diagnosis model of radio frequency impedance matching in plasma equipment by using neural network and wavelets

Byungwhan Kim, Jae Young Park, Dong Hwan Kim, Seung Soo Han

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A new calibration model for plasma diagnosis was constructed by combining radio frequency impedance match data, wavelet, and neural network. A total of 30 fault symptoms were simulated with the variations in the four process parameters. Both discrete wavelet transformation (DWT) and continuous wavelet transformation (CWT) were utilized to filter the sensor information. Three types of diagnosis models (raw-, DWT-, and CWT-based models) were constructed. The comparisons revealed that the improvement in the prediction performance of DWT and CWT data models over the raw data model were about 42% and 30%, respectively.

Original languageEnglish
Title of host publicationPRICAI 2006
Subtitle of host publicationTrends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings
PublisherSpringer Verlag
Pages995-999
Number of pages5
ISBN (Print)3540366679, 9783540366676
DOIs
StatePublished - 2006
Event9th Pacific Rim International Conference on Artificial Intelligence - Guilin, China
Duration: 7 Aug 200611 Aug 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4099 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Pacific Rim International Conference on Artificial Intelligence
Country/TerritoryChina
CityGuilin
Period7/08/0611/08/06

Fingerprint

Dive into the research topics of 'Diagnosis model of radio frequency impedance matching in plasma equipment by using neural network and wavelets'. Together they form a unique fingerprint.

Cite this