利用深度神經網路模型換算表面電漿共振增強 Goos-Hänchen 位移

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李政宏(Cheng-Hung Lee) 查詢紙本館藏

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光電科學與工程學系

論文名稱

利用深度神經網路模型換算表面電漿共振增強 Goos-Hänchen 位移
(Using deep neural network (DNN) model to transform the surface plasma resonance (SPR) signal into its corresponding Goos-Hänchen shift)

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摘要(中)

近年來將表面電漿共振應用於生物傳感器已在生物研究、健康科學研究、藥物發現、臨床診斷、環境和農業監測等領域非常流行，它可檢測傳感器表面的折射率變化，且擁有即時檢測、非標記、高靈敏度等優點，在市面上也有越來越多的商用儀器證明了 SPR 生物傳感器的成功。由於SPR生物傳感器在近年來得到了相當大的進展，本論文將利用機器學習中的深度神經網路對在表面電漿共振下之TE波、TM波之干涉現象搭配Goos–Hänchen位移進行學習，建立出一套Goos–Hänchen位移之生物傳感器模型，模型建立後，將各濃度之甘油水溶液丟入模型量測，並量測其靈敏度與檢測極限，可得到Goos–Hänchen位移之生物傳感器靈敏度為為29590μm/RIU，檢測極限為7.14∙〖10〗^(-6) RIU，相較於傳統的SPR反射強度量測，檢測極限整整多出了兩個量級，這不僅證實了相位式SPR量測在表現上優於反射強度量測，也透過訓練上參數的選擇證實了Goos–Hänchen位移量測靈敏度的增強來自於共振時的奇異相位延遲，而也因Goos–Hänchen位移與相位息息相關，因此Goos–Hänchen位移SPR生物傳感器也提供了另一種相位式SPR量測方法，相較於傳統相位式量測大大簡化了光學元件的運用。

摘要(英)

In recent years, the application of surface plasmon resonance to biosensors (SPR Biosensor) has become very popular in the fields of biological research, health science research,drug discovery, clinical diagnosis,environmental and agricultural monitoring, etc. It can detect changes in the refractive index of the sensor surface, and with the advantages of instant detection,lable-free,and high sensitivity, more and more commercial instruments on the market have proved the success of SPR biosensors.As SPR biosensors have made considerable progress in recent years, this paper will use the deep neural network in machine learning to learn the interference phenomena of TE waves and TM waves under surface plasmon resonance with Goos–Hänchen displacement, and establish a set of biosensor models for Goos–Hänchen displacement. After the model was established,the glycerol solution of various concentrations was dropped into the model for measurement,and its sensitivity and detection limit were measured. The sensitivity of the biosensor for Goos–Hänchen displacement can be obtained as 29590μm/RIU, the detection limit is 7.14∙〖10〗^(-6) RIU.Compared with the traditional SPR reflection intensity measurement,the detection limit is two orders of magnitude more,which not only confirms that the phase SPR measurement is better than the reflection intensity measurement, but also confirms that the increase in the sensitivity of the Goos–Hänchen displacement measurement is due to the singular phase delay at resonance. And because the Goos–Hänchen displacement is closely related to the phase, the Goos–Hänchen displacement SPR biosensor also provides another phase-based SPR measurement method, which greatly simplifies the use of optical components compared to the traditional phase-based measurement.

關鍵字(中)

★ 深度神經網路
★ 表面電漿共振

關鍵字(英)

★ deep neural network
★ surface plasma resonance

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 vii
表目錄 x
第一章緒論 1
1-1研究背景與動機 1
1-2生物傳感器文獻回顧 2
1-3深度神經網路簡介 6
第二章基礎理論 11
2-1 GOOS–HÄNCHEN 效應 11
2-2表面電漿共振(SURFACE PLASMON RESONANCE) 13
2-3多層膜之反射與透射 18
2-4 梯度下降學習法 25
第三章實驗架構 28
3-1 EO 相位調變 28
3-2 研究架構 31
3-3 研究方法 32
第四章實驗結果 34
4-1 模擬結果 35
4-2 實驗結果與分析 37
4-2-1 Goos–Hänchen位移量測-PSD 37
4-2-2 Goos–Hänchen位移量測-AI模型 38
4-3 實驗結果探討與改善 41
第五章結論 45
參考文獻 46

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指導教授

郭倩丞(Chien-Cheng Kuo)

審核日期

2021-9-1

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