Prediction of Chamber Leak Pattern Using Time-Series Neural Network

被引：0

作者：

Kwon, Mine ^{[1
]}

Park, Byung Chan ^{[2
]}

Kim, Byungwhan ^{[1
]}

机构：

[1] Sejong Univ, Dept Elect Engn, 98 Kunja Dong, Seoul 143747, South Korea

[2] Semisysco Inc, Suwon, South Korea

来源：

NEW ASPECTS OF SYSTEMS, PTS I AND II | 2008年

关键词：

Plasma; Leak: Prediction; Time-series neural network; Monitoring; Detection; Diagnosis; Optical emission spectroscopy;

D O I：

暂无

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

A leak of plasma chamber should be strictly monitored to maintain process quality as well as device yield. Using an Auto-Correlated Time Series Neural Network (A-NTS), a prediction model of chamber leak was developed. A total of 47 leak patterns were used to construct and test the monitoring efficacy of model. The validation errors for normal and abnormal data ranged between 51.9 and 61.7, and between 126.0 and 163.8, respectively. The validation errors for the abnormal data were more than two times larger than those for the nor-Mal data. This clearly indicates that the A-NTS model can accurately detect a leak from the chamber of plasma equipment.

引用

页码：164 / +

页数：2

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