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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林致廷 | |
| dc.contributor.author | Wei-En Hsu | en |
| dc.contributor.author | 徐偉恩 | zh_TW |
| dc.date.accessioned | 2021-06-17T09:06:56Z | - |
| dc.date.available | 2024-12-26 | |
| dc.date.copyright | 2019-12-26 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-12-19 | |
| dc.identifier.citation | References
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74750 | - |
| dc.description.abstract | 由於物聯網的發展，次世代的感測器研究正在發生典範轉移。如今，多模態感測器已經在物聯網及人工智慧應用中扮演不可或缺的角色。為了更進一步降低功率及感測器尺寸，多模態感測元件將是未來感測技術的重要趨勢。因此，在本論文中，我們利用雙閘極離子感測場效電晶體成功實現多模態感測元件，成功同時量化酸鹼度，光及溫度三種物理變量。並提出虛擬感測器空間的模型來解釋多模態感測原理。首先，本論文提出利用時序產生虛擬感測器的理論，並解釋虛擬感測器空間與多重感測效應的關聯性。隨後，我們成功實現並驗證酸鹼度/光度雙重感測器以及酸鹼度/光/及溫度三重感測器，並且利用感測器的量測數據驗證我們所提出之虛擬感測空間的模型。利用本論文提出的虛擬感測器空間與多模態感測原理，我們可以將多模態感測器實現於單一感測元件中，並應用於感測器融合，物聯網以及人工智慧辨識等應用。 | zh_TW |
| dc.description.abstract | Internet-of-thing (IoT) has driven a paradigm shift to the next-generation sensor architecture. Nowadays, multi-modal sensors are essential for various IoT and AI applications. To achieve ultra-low power and size reduction, single-device-multi-sensor could be a promising direction of sensor technologies. Therefore, we have realized a machine learning-assisted multi-modal sensing device based on a dual-gate ion-sensitive field-effect transistor (DG-ISFET) and proposed a virtual sensor space model to effectively explain the fact of multi-sensing. In the first part, we propose a sequential method to generate virtual sensors and a virtual sensor space model to connect the physical sensing device and multi-sensing functionalities. Afterwards, we successfully realized and validated a pH/light dual-modal sensor and a pH/light/temperature 3-dimensional multi-modal sensor. In addition, the virtual sensor space model is also validated. With the proposed virtual sensor space model, multi-modalities sensing can be achieved on single device. It has great potential applying to sensor fusion and AI recognition technologies. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T09:06:56Z (GMT). No. of bitstreams: 1 ntu-108-D04943015-1.pdf: 4118855 bytes, checksum: 9918a5a8562e0ff263077bcc6d7cee08 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | Content
誌謝 I 摘要 II Abstract III Content IV List of Figures VIII List of Tables XI Chapter 1. Introduction 1 1-1 Background and Motivation of IoT Sensor Researches 1 1-2 Sensor Technology Review 3 1-3 The Structure of this Thesis 7 Chapter 2. Dual-gate ISFET Measurement and Characterization 9 2-1 Fundamental and Sensing Principle of ISFET Biochemical Sensors 10 2-2 ISFET Subtypes 14 2-2.1 Conventional ISFET 14 2-2.2 Dual-gate ISFET 15 2-3 Non-ideal Characteristics of ISFET 17 2-3.1 Photo-effect 17 2-3.2 Temperature Effect 18 2-4 DG-ISFET Design, Fabrication, and Measurement Setup 19 2-4.1 Design and Fabrication 19 2-4.2 Measurement Setup 22 2-4.3 pH and Light Measurement Sequences 23 2-4.4 Temperature Measurement Setup 24 2-5 Dual-gate ISFET Characterization 25 2-5.1 pH Sensing Discussion 25 2-5.2 Photo-response 30 2-5.3 Thermal Response 34 2-6 Chapter Conclusion 35 Chapter 3 The Concept of Virtual Sensor and Machine Learning Approaches 36 3-1 Introduction 36 3-2 Sequentially Generated Virtual Sensor 36 3-3 Virtual Sensor Space 39 3-4 Machine Learning Models and Settings 46 3-4.1 Support Vector Machine 47 3-4.2 Artificial Neural Network 48 Chapter 4 Dual-Modal Sensing ISFET Realization and Validation 50 4-1 Measured Data Analysis and Discussions 51 4-2 Visualized Data by Features 53 4-2.1 Data Pre-Processing 54 4-3 Modeling and Validation 56 4-3.1 Semi-Quantification Models 57 4-3.2 Quantification Models 60 4-3.3 Cross-Validation 63 4-3.4 Feature reduction 66 4-4 Chapter Conclusion 68 Chapter 5 Three-Dimensional Multi-Modal Sensing ISFET and Virtual Sensor Space Validation 70 5-1 Introduction 70 5-2 Sensor Measurement and Data Collection 71 5-2.1 Sensing Device Characteristics and Virtual Sensor Generation 73 5-3 Multi-Modal Sensing Model and Validation 75 5-3.1 Operational Orthogonality 77 5-3.2 Intrinsic Orthogonality 81 5-3.3 Effect of Neural Network Structure 84 5-4 Chapter Conclusion 86 Chapter 6 Conclusions and Future Works 88 References 91 | |
| dc.language.iso | en | |
| dc.subject | 多模態感測器 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 離子感測場效電晶體 | zh_TW |
| dc.subject | 物聯網感測器 | zh_TW |
| dc.subject | 元件物理 | zh_TW |
| dc.subject | ISFET | en |
| dc.subject | machine learning | en |
| dc.subject | Multi-modal sensor | en |
| dc.subject | device physics | en |
| dc.subject | IoT sensor | en |
| dc.title | 利用機器學習於單一電晶體上實現多重模態感測效果 | zh_TW |
| dc.title | A Machine Learning-assisted Multi-modality Sensor Based on a Single Transistor | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 林啟萬,吳安宇,鄭桂忠,劉怡劭,王玉麟 | |
| dc.subject.keyword | 多模態感測器,機器學習,離子感測場效電晶體,物聯網感測器,元件物理, | zh_TW |
| dc.subject.keyword | Multi-modal sensor,machine learning,ISFET,IoT sensor,device physics, | en |
| dc.relation.page | 108 | |
| dc.identifier.doi | 10.6342/NTU201904386 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-12-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
| 顯示於系所單位： | 電子工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-108-1.pdf 未授權公開取用 | 4.02 MB | Adobe PDF |
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