Feature Extraction of Radio Frequency Signal for Fault Detection of Plasma Etching Equipment
碩士 === 中原大學 === 機械工程研究所 === 96 === Etch process is an important and indispensable process in the semiconductor manufacturing for removing the pattern defined by photolithography process. Most etched profiles cannot be reworked. Thus, the accuracy of critical dimension, etch depth and etch uniformity...
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ndltd-TW-096CYCU54890552015-10-13T14:53:13Z http://ndltd.ncl.edu.tw/handle/85022142379717843193 Feature Extraction of Radio Frequency Signal for Fault Detection of Plasma Etching Equipment 基於射頻訊號特徵萃取之電漿蝕刻設備故障偵測 Tien-Yu Ku 古典諭 碩士 中原大學 機械工程研究所 96 Etch process is an important and indispensable process in the semiconductor manufacturing for removing the pattern defined by photolithography process. Most etched profiles cannot be reworked. Thus, the accuracy of critical dimension, etch depth and etch uniformity depends on the stringently control of etch equipment. However, every mechanism has a chance to decline or damage. Fault detection becomes an important subject in the semiconductor manufacturing. Self organizing feature map network is an artificial neural network with unsupervising competition learning. Input data will be mapped to the output units by self organizing and, finally, assigned in the output space with meaningful topological structure by output neurons according to input data’s vector characteristics. Thus, self organizing feature map network transfers higher dimensional input data to lower dimensional spacial figures. In this thesis, the self organizing feature map network was used for fault detection of radio frequency (RF) power of etch equipment. The waveform of RF power is monitored by self organizing feature map network to prevent faulty process so as to reduce the cost loss in the semiconductor manufacturing. The first step of experiments was to collect 10 sets of normal signals of RF power for offline training. Through the self organizing feature map network, an ellipse was calculated to cover all outputs of neural network and defined as threshold limit. Then, this trained result was implemented to fault detection of etch equipment. A total of 400 waveforms of RF power, i.e. 400 runs of etch processes, were monitored. Among them, 6 abnormal waveform signals were detected and classified into 5 kinds of faults of RF system. The results show that this proposed approach of fault detection has excellent performance. Yaw-Jen Chang 張耀仁 2008 學位論文 ; thesis 84 zh-TW |
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碩士 === 中原大學 === 機械工程研究所 === 96 === Etch process is an important and indispensable process in the semiconductor manufacturing for removing the pattern defined by photolithography process. Most etched profiles cannot be reworked. Thus, the accuracy of critical dimension, etch depth and etch uniformity depends on the stringently control of etch equipment. However, every mechanism has a chance to decline or damage. Fault detection
becomes an important subject in the semiconductor manufacturing.
Self organizing feature map network is an artificial neural network with unsupervising competition learning. Input data will be mapped to the output units by self organizing and, finally, assigned in the output space with meaningful topological structure by output neurons according to input data’s vector characteristics. Thus, self organizing feature map network transfers higher
dimensional input data to lower dimensional spacial figures. In this thesis, the self organizing feature map network was used for fault detection of radio frequency (RF) power of etch equipment. The waveform of RF power is monitored by self organizing feature map network to prevent faulty process so as to reduce the cost
loss in the semiconductor manufacturing.
The first step of experiments was to collect 10 sets of normal signals of RF power for offline training. Through the self organizing feature map network, an ellipse was calculated to cover all outputs of neural network and defined as threshold limit. Then, this trained result was implemented to fault detection of etch equipment. A total of 400 waveforms of RF power, i.e. 400 runs of etch processes, were monitored. Among them, 6 abnormal waveform signals were detected and classified into 5 kinds of faults of RF system. The results show that
this proposed approach of fault detection has excellent performance.
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Yaw-Jen Chang |
author_facet |
Yaw-Jen Chang Tien-Yu Ku 古典諭 |
author |
Tien-Yu Ku 古典諭 |
spellingShingle |
Tien-Yu Ku 古典諭 Feature Extraction of Radio Frequency Signal for Fault Detection of Plasma Etching Equipment |
author_sort |
Tien-Yu Ku |
title |
Feature Extraction of Radio Frequency Signal for Fault Detection of Plasma Etching Equipment |
title_short |
Feature Extraction of Radio Frequency Signal for Fault Detection of Plasma Etching Equipment |
title_full |
Feature Extraction of Radio Frequency Signal for Fault Detection of Plasma Etching Equipment |
title_fullStr |
Feature Extraction of Radio Frequency Signal for Fault Detection of Plasma Etching Equipment |
title_full_unstemmed |
Feature Extraction of Radio Frequency Signal for Fault Detection of Plasma Etching Equipment |
title_sort |
feature extraction of radio frequency signal for fault detection of plasma etching equipment |
publishDate |
2008 |
url |
http://ndltd.ncl.edu.tw/handle/85022142379717843193 |
work_keys_str_mv |
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