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研究生:曾世杰
研究生(外文):Shah-Jye Tzeng
論文名稱:奈米金屬氧化物半導體-基氣體感測材料特性及應用類神經網路於多重感測器之氣體辨識
論文名稱(外文):The Properties of Nanocrystalline Metal Oxide Semiconductor - based Gas Sensor Materials and Gases Identification by Neural networks and Multi-gas Sensors
指導教授:林鴻明林鴻明引用關係
指導教授(外文):Hong-Ming Lin
學位類別:博士
校院名稱:大同大學
系所名稱:材料工程研究所
學門:工程學門
學類:材料工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:英文
論文頁數:159
中文關鍵詞:氧化鋅氧化鎢感測器類神經網路
外文關鍵詞:ZnOWO3SensorNeural Network
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摘 要
金屬氧化物氣體感測材料對存在於空氣中的可燃性氣體,如CH4、LPG等有很高的靈敏度。在文獻中有些例子是利用這類感測材料的薄膜或厚膜製成的感測器,成功的偵測到空氣中可燃性氣體的存在。
本研究是利用奈米金屬氧化物半導體基氣體感測材料所作成的感測器,如氧化鋅基、氧化鎢基及添加貴金屬Pd氧化鎢基氣體感測器,並將其組成一感測器陣列。在完成感測器的製作,氧化鋅材料的平均粒徑約為75 nm;氧化鎢則為30 nm。在對一氧化碳、二氧化碳及二氧化氮的偵測上,本實驗所用的奈米氧化鋅基氣體感測器對二氧化氮氣體的靈敏度為128,這是目前實驗中靈敏度最高的。
選擇性一直是金屬氧化物半導體感測器最大的問題,在文獻上有添加貴金屬以提高選擇的,成功的例子也不少。本研究則利用類神經網路於多重感測器上之氣體辨識,並成功的辨識一氧化碳、二氧化碳、及二氧化氮等。其間也嘗試對不同濃度的氣體,及混合氣的辨識均有很高的成功率。因此利用類神經網路於多重感測器的方法亦可補償金屬氧化物對氣體選擇性的不足。

ABSTRACT
Metal oxide gas sensing materials are known those have high sensitivity due to inflammable gases in air such as CH4, LPG. There are some succeeding examples with thin or thick film gas sensors in detecting these gases have been reported.
In this study, Nanocrystalline metal oxide semiconductor gas sensor array had been constructed. There are three components in this gas sensor array: ZnO-based, WO3-based, and Pd doped WO3-based gas sensors. While completely finished nanocrystalline gas sensors, the mean particle size of ZnO-based gas sensors is about 80 nm; and the WO3-based is about 30 nm. When detecting NO2 gas, the sensitivity of our NC ZnO-based sensor approached to 128, this result is the highest value in the current reports. We constructed these three gas sensors into detected chamber and then detecting various gases.
The selective problem is the defect of the most metal oxide semiconductor gas sensors. Added noble metal, such as Pt, Pd, might increase selective property. Artificial neural network technique to identify various gases has been examined in this study. The neural network can recognize CO, CO2, NO2 and CO + NO2 precisely.

封面
CHINESE ABSTRACT
ENGLISH ABSTRACT
ACKNOWLEDGEMENTS
CONTENTS
LIST OF TABLES
LIST OF FIGURES
CHAPTER 1 INTRODUCTION
CHAPTER 2 LITERATURE REVIEW
2.1 Introduction of NCM
2.1-1 The Characteristics of NCM
2.1-2 The Synthesis Technologies and Application of NCM
2.1-3 The Advantages of NCM
2.1-4 The Analysis of NCM
2.2 Synthesis of Zinc Oxides
2.2-1 Chemical Deposition Method
2.2-2 Sputtering Method
2.2-3 Gas Evaporation Method
2.3 Synthesis of Tungsten Oxides
2.3-1 Chemical Deposition Method
2.3-2 Sputtering Method
2.3-3 Gas Condensation Method
2.4 Gas Sensors
2.4-1 Fundamentals of Semiconductor Gas Sensors
2.4-2 ZnO-based Gas Sensors
2.4-3 WO3-based Gas Sensors
2.5 Introduction of Artificial Neural Networks
2.6 History of ANNs
2.7 Basic Concepts of Neural Networks
2.7-1 Net work Properties
2.7-2 Node Properties
2.7-3 System Dynamics
2.8 Fundamentals of Back-propagation
2.8-1 The Biological Neuron
2.8-2 The Artificial Neuron
2.8-3 Two-layer Back-propagation Network
2.8-4 Training(Supervised Training)
2.8-5 Momentum
2.8-6 Recalling
2.8-7 Gas/Odor Identification by Gas-Sensor Array
CHAPTER 3 EXPERIMENT
3.1 ZnO Gas Sensor Fabrication
3.2 WO3 Gas Sensor Fabrication
3.3 WO3 Gas sensor Doped with NC Pd
3.4 Constructed a Multiple Gas Sensor
3.5 The properties of gas sensor
3.6 Sensitivity Measurement
3.7 Artificial neural network
CHAPTER 4 RESULTS AND DISCUSSION
4.1 Properties of Metal oxide semiconductor gas sensor
4.1-1 Properties of ZnO gas sensor
4.1-2 Properties of NC WO3 gas sensor
4.1-3 Properties of Pd doped NC WO3 gas sensor
4.2 Measurement of Sensitivity
4.2-1 CO gas detection
4.2-2 CO2 gas detection
4.2-3 NO2 gas detection
4.3 Compared Our Results with the Current Papers
4.4 Compared a Sensor aaray
4.5 Pattern Recognition
4.5-1 Training the Input Data
4.5-2 Recognition
4.5-3 Pattern Recognition Including Gases Species And its Concentrations
CHAPTER 5 CONCLUSIONS
CHAPTER 6 THE FUTURE WORKS
REFERENCES

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