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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 薛文証 | zh_TW |
| dc.contributor.advisor | Wen-Jeng Hsueh | en |
| dc.contributor.author | 林配德 | zh_TW |
| dc.contributor.author | Pei-Te Lin | en |
| dc.date.accessioned | 2025-07-25T16:04:36Z | - |
| dc.date.available | 2025-07-26 | - |
| dc.date.copyright | 2025-07-25 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-21 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98100 | - |
| dc.description.abstract | 機器學習技術與光電元件的結合已成為提升現代感測平台功能性、智慧性和適用性的變革性方法。受低功耗、高選擇性和即時感測需求的驅動，本論文對機器學習在室溫條件下運行的光電感測器中的應用進行了全面的研究。
探討了三種相互關聯的感測策略。首先開發了一種光化學激活氣體感測平台，利用紫外線激發實現穩定、高效的氣體檢測，而無需加熱。其次，提出了一種光學調製氣體識別系統，其中透過改變照明強度產生的不同光學指紋透過機器學習模型進行分析，包括支援向量機、樸素貝葉斯、隨機森林和 K 近鄰演算法，以實現準確的多氣體分類和濃度預測。第三，製造了基於自供電異質接面結構的寬頻光電探測器陣列，展示了使用機器學習輔助波長識別對紫外線、可見光和近紅外線區域的多光譜光訊號進行分類和重建的能力。 透過將材料創新與機器學習驅動的數據處理相結合，這項工作建立了智慧氣體感測和光學檢測的統一框架，促進了可擴展、節能和高度適應的下一代感測技術。研究結果強調了機器學習在提高光電設備性能和多功能性方面的關鍵作用，為未來的智慧感測應用奠定了基礎。 | zh_TW |
| dc.description.abstract | The integration of machine learning (ML) techniques with optoelectronic devices is emerging as a transformative approach to advancing the functionality, intelligence, and applicability of modern sensing platforms. Motivated by the demand for low-power, high-selectivity, and real-time sensing, this dissertation presents a comprehensive study on the application of ML in optoelectronic sensors operating under room temperature (RT) conditions.
Three interrelated sensing strategies are explored. First, a photochemically activated gas sensing platform is developed, utilizing ultraviolet (UV) excitation to enable stable and efficient gas detection without the need for thermal heating. Second, an optically modulated gas identification system is proposed, in which distinct optical fingerprints generated through varying illumination intensities are analyzed by ML including Support Vector Machines (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) to achieve accurate multi-gas classification and concentration prediction. Third, a broadband photodetector array (PDA) based on a self-powered heterojunction structure is fabricated, demonstrating the ability to classify and reconstruct multispectral light signals across UV, visible (Vis), and near-infrared (NIR) regions using ML-assisted wavelength recognition. By integrating material innovation with ML-driven data processing, this work establishes a unified framework for intelligent gas sensing and optical detection, promoting scalable, energy-efficient, and highly adaptive next-generation sensing technologies. The results underline the critical role of ML in enhancing the performance and versatility of optoelectronic devices, laying a foundation for future smart sensing applications. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-25T16:04:36Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-25T16:04:36Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 論文口試委員會審定書 i
謝辭 ii 摘要 iii Abstract iv Contents vi List of Figures x List of Tables xviii List of Symbols xix List of Abbreviations xx Chapter 1 Introduction 1 1.1 Background 1 1.2 Literature review 3 1.2.1 Light-activated gas sensing 3 1.2.2 PDAs and multi-wavelength recognition development 7 1.2.3 Machine learning for gas sensors and photodetectors 9 1.3 Research objectives 12 Chapter 2 Theoretical Background and Methodology 14 2.1 Sensing mechanisms of the devices 14 2.1.1 Photoactivated gas sensing mechanism of metal oxide semiconductors 14 2.1.2 Photodetection mechanism of self-powered heterojunction device 21 2.2 Measurement systems and experimental setup 23 2.2.1 X-ray Diffraction (XRD) 23 2.2.2 Scanning Electron Microscopy (SEM) 24 2.2.3 Energy Dispersive Spectroscopy (EDS) 25 2.2.4 Atomic Force Microscopy (AFM) 26 2.2.5 X-ray Photoelectron Spectroscopy (XPS) 27 2.2.6 UV-Visible Spectroscopy (UV-vis) 28 2.2.7 Measurement system for gas sensing 29 2.2.8 Measurement System for PDA-based optical imaging 31 2.3 Definition of sensing parameters 32 2.3.1 Definition of gas sensing performance parameters 32 2.3.2 Definition of Photodetector performance parameters 33 2.4 Machine learning algorithms for classification 34 2.4.1 Support Vector Machines (SVM) 34 2.4.2 Random Forest (RF) 36 2.4.3 Naïve Bayes (NB) 39 2.4.4 K-Nearest Neighbors (KNN) 40 2.5 Overview of neural networks 43 2.5.1 Artificial Neural Networks (ANN) 43 2.5.2 Multilayer Perceptron (MLP) 44 2.5.3 Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm 46 2.6 Feature extraction and Principal Component Analysis (PCA) 46 2.7 Regression models for prediction 48 Chapter 3 Photochemically-Activated p-Type CuGaO2 Thin Films for Highly-Stable Room-Temperature Gas Sensors 51 3.1 Introduction 51 3.2 Materials and methods 52 3.2.1 Fabrication of CuGaO2 gas sensor 52 3.2.2 Materials analysis & device characterization 52 3.3 Results and discussion 53 3.3.1 Material analysis 53 3.3.2 Gas sensing characteristics 58 3.3.3 Long-term stability and humidity influence evaluation 66 3.4 Conclusions 68 Chapter 4 Optical Fingerprint for Gas Identification at Room Temperature using Light-Activated a-IGZO Thin Films and Machine Learning 69 4.1 Introduction 69 4.2 Materials and methods 70 4.2.1 Fabrication of a-IGZO gas sensor 70 4.2.2 Materials analysis & device characterization 71 4.2.3 Machine learning-based data processing 72 4.3 Results and discussion 73 4.3.1 Material Characterization 73 4.3.2 Gas sensing performance 78 4.3.3 Optical fingerprints and PCA analysis 82 4.3.4 Machine learning-based gas identification 88 4.3.5 Machine learning-based prediction of gas concentrations 96 4.3.6 Investigation of operational stability and humidity impact 99 4.4 Conclusions 102 Chapter 5 Machine Learning Assisted Wavelength Recognition in Cu2O/Si Self-Powered Photodetector Arrays for Advanced Image Sensing Applications 104 5.1 Introduction 104 5.2 Materials and methods 107 5.2.1 Fabrication of Cu2O/Si PDA 107 5.2.2 Materials analysis & device characterization 108 5.2.3 Machine learning-based data processing 109 5.3 Results and discussion 110 5.3.1 Material characterization 110 5.3.2 Cu2O/Si PDA characteristics in the dark and under UV Illumination 115 5.3.3 Energy band structure of Cu2O/Si heterojunction interface 117 5.3.4 Wavelength classification using machine learning algorithms 125 5.3.5 Light intensity prediction using regression models 132 5.3.6 Light intensity impact 134 5.4 Conclusions 138 Chapter 6 Conclusions 139 6.1 Summary 139 6.2 Suggestion for future research 140 Chapter 7 References 141 | - |
| dc.language.iso | en | - |
| dc.subject | 波長識別 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 光學指紋 | zh_TW |
| dc.subject | 光電感測器陣列 | zh_TW |
| dc.subject | 光激活氣體感測器 | zh_TW |
| dc.subject | Machine learning | en |
| dc.subject | Optical fingerprints | en |
| dc.subject | wavelength recognition | en |
| dc.subject | photo detector array | en |
| dc.subject | photochemically-activated gas sensor | en |
| dc.title | 機器學習應用於光電感測元件之研究 | zh_TW |
| dc.title | A Study on the Application of Machine Learning in Optoelectronic Devices | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.coadvisor | 黃俊穎 | zh_TW |
| dc.contributor.coadvisor | Chun-Ying Huang | en |
| dc.contributor.oralexamcommittee | 陳正雄;彭朋群;王志銘;蕭惠心 | zh_TW |
| dc.contributor.oralexamcommittee | Jeng-Shiung Chen;Peng-Chun Peng;Chih-Min Wang;Hui-Hsin Hsiao | en |
| dc.subject.keyword | 機器學習,波長識別,光電感測器陣列,光學指紋,光激活氣體感測器, | zh_TW |
| dc.subject.keyword | Machine learning,photochemically-activated gas sensor,Optical fingerprints,photo detector array,wavelength recognition, | en |
| dc.relation.page | 156 | - |
| dc.identifier.doi | 10.6342/NTU202501465 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-07-22 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
| dc.date.embargo-lift | 2030-07-01 | - |
| Appears in Collections: | 工程科學及海洋工程學系 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-113-2.pdf Restricted Access | 44.81 MB | Adobe PDF | View/Open |
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