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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 林致廷(Chih-Ting Lin) | |
| dc.contributor.author | Yi-Ting Wu | en |
| dc.contributor.author | 吳奕霆 | zh_TW |
| dc.date.accessioned | 2022-11-25T06:33:22Z | - |
| dc.date.copyright | 2021-08-16 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-28 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82186 | - |
| dc.description.abstract | 由於人類對於健康維持、醫療照護之需求以及迅速發展之科技，造就了現今醫療物聯網的時代，利用微型化裝置、穿戴式元件等醫療設備，能隨時將身體之健康資訊傳至雲端中，並藉由遠端醫療技術進行監控，以有效管理自己的健康；因此，用以接收生理訊號之感測器即扮演了十分重要的角色，如何設計低功耗、低成本、微型化且具備著高靈敏度的感測器為當前非常熱門之研究方向。其中，透過人體生理液中各種離子濃度，可對許多疾病、症狀進行篩檢，因此若能快速進行生理液中離子濃度的檢測，那麼就可以隨時地掌握自身之健康狀況。 為了達成上述之離子檢測，本論文以台積電0.18μm製程製作之雙閘極離子感測場效電晶體作為量測平台，因其相容於標準CMOS製程而能以低成本進行大量製造且能夠被微型化，故適合應用為醫療物聯網之感測器，此外我們將配製用來模擬生理液之混合溶液，以進行複數離子感測的實驗；然而，由於特定離子之低靈敏度與離子競爭性，我們所採用之感測器無法分辨每種離子所造成的影響，故無法進行濃度值之分析，而傳統上會在感測層沉積離子選擇膜以克服該問題，但是該製程會使成本增加且不易於大量製造。因此本論文提出物理場感測模型之概念，藉由額外之溫度場或電場對感測機制進行非線性轉換，使各種離子種類、濃度於不同溫度場或電場下均有相異之特徵，再結合特徵工程與人工神經網路，將該特徵用於訓練模型，最後訓練完成的感測模型，能成功地推論出混合溶液中每種離子的濃度。由研究結果顯示，透過物理場與演算法之結合，可以使感測器於未來有更上一層樓的能力。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T06:33:22Z (GMT). No. of bitstreams: 1 U0001-2807202102212500.pdf: 4507339 bytes, checksum: 91512e279a2ca6cae0111f3378228e64 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 I 誌謝 II 摘要 III Abstract IV 圖目錄 VII 式目錄 IX 表目錄 X 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究大綱 2 第二章 感測元件原理介紹及實驗方法 3 2.1 離子感測場效電晶體 3 2.2 雙閘極離子感測場效電晶體 5 2.3 感測器陣列晶片 7 2.4 基於離子感測場效電晶體之多離子感測 8 2.5 應用於建立感測模型之演算法 10 2.6 多離子感測模型之架構 13 第三章 溫度場感測模型 15 3.1 離子感測場效電晶體之溫度效應 15 3.2 量測溶液之配製 16 3.3 資料集之預處理與分割 17 3.4 多層感知器 18 3.5 模型之推論結果與討論 19 第四章 多通道訊號之電場感測模型 21 4.1 離子感測場效電晶體之電場效應 21 4.2 量測溶液之配製 22 4.3 資料集之預處理與分割 23 4.4 卷積神經網路 25 4.5 模型之推論結果與討論 26 第五章 壓縮訊號之電場感測模型 28 5.1 場效電晶體之雙閘極非線性特性 28 5.2 量測溶液之配製 29 5.3 資料集之預處理與分割 30 5.4 多層感知器 33 5.5 模型之推論結果與討論 34 第六章 結論 36 6.1 成果總結 36 6.2 未來展望 37 參考文獻 38 | |
| dc.language.iso | zh-TW | |
| dc.subject | 人工神經網路 | zh_TW |
| dc.subject | 醫療物聯網感測器 | zh_TW |
| dc.subject | 離子感測場效電晶體 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | ANN | en |
| dc.subject | Machine Learning | en |
| dc.subject | HIoT Sensor | en |
| dc.subject | ISFET | en |
| dc.title | 利用機器學習於離子感測場效電晶體進行多離子感測 | zh_TW |
| dc.title | Using Machine Learning for Multi-ion Detection Based on Ion-Sensitive Field-Effect Transistor | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 楊家驤(Hsin-Tsai Liu),王玉麟(Chih-Yang Tseng) | |
| dc.subject.keyword | 醫療物聯網感測器,離子感測場效電晶體,機器學習,人工神經網路, | zh_TW |
| dc.subject.keyword | HIoT Sensor,ISFET,Machine Learning,ANN, | en |
| dc.relation.page | 44 | |
| dc.identifier.doi | 10.6342/NTU202101829 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-07-28 | |
| 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 |
| dc.date.embargo-lift | 2023-07-09 | - |
| Appears in Collections: | 電子工程學研究所 | |
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| File | Size | Format | |
|---|---|---|---|
| U0001-2807202102212500.pdf Restricted Access | 4.4 MB | Adobe PDF |
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