光蝕刻技術模擬以及生理訊號之傳輸與分析

本論文分成兩個部分，一個為光製程之蝕刻技術模擬，將整個微顯影製程當中之光學系統完整模擬，包括光化學反應模擬，光阻之建立以及其反應還有化學擴散之效應；隨著超大型積體電路(VLSI, very-large-scale integrated-circuit)製程技術的進步，元件的特徵尺寸(feature size)已遠小於曝光光源的波長，微顯影成像系統(micro-lithography image system)的解析極限不斷的被挑戰，成像結果因繞射效應明顯地偏離了原本的設計圖樣(design pattern)。另外在本論文中，我們深入討論光阻層之模擬，針對Dill、Kim、Mack及加強型Mack顯影模型的建立方式做介紹，討論各模型的優劣。並深入去討論光之駐波(Standing Wave)對設計圖樣之影響，另外就其化學反應的擴散現象，利用數值方法來模擬化學之酸擴散(Acid diffusion)之現象。製程上使用的化學放大光阻，在較低能量之光線反應上較好，其後續的反應可以看為一個酸擴散之現象，對於此擴散方程式，我們深入去討論並解析，並引入矩陣代數以及一些演算法，以及快速傅立葉轉換的方式來計算其數值解，以期對整個光製程之模擬做出一個可靠且快速的輔助系統。另一主題為生理訊號之傳輸與分析，遠距照護(Telecare)之議題隨著科技之進步，以及對於醫療資源之善用有著卓越之改善，因此建立一套系統來為無縫式個人健康記錄之行動裝置提供，針對無線傳輸之生理量測儀器與智慧型手機應用，以行動個案為對象，於家中透過生理監測儀器量測數值後，經由藍芽無線傳輸以及無線網路之系統，上傳至個人化健康照護整合平台。透過智慧型手機的輔助，可達到不受時間、地點限制，均可透過手機上網存取個人健康記錄、自我管理、甚至於看診時提供醫師參考，同時透過低功耗的藍牙傳輸技術，將無線感應生理訊號量測儀器傳輸至智慧型手機，透過智慧型手機的方便性與方便攜帶性，使得資料的分析與閱讀可即時進行。系統目前完成了血壓之量測並上傳，另外還有在心電圖之分析，利用支持向量機 (Support Vector Machines)整合MIT-BIH Arrhythmia database，來當網路後端之完整分析系統，在手機上可以接收並且即時的將心電圖繪製在手機螢幕上，在系統時間到達時會自動的送往後端之分析系統，並可以立即在手機上看到分析之結果；除此之外，我們將一般心臟疾病所要看之重要數值，以及心肌梗塞之判端方式，提出一套即時(real-time)的演算法，實現在智慧型手機上，使其可以做到傳輸兼具簡單的監測，使遠距照護不再只是空談。

關鍵字

投影式光學微顯影；光阻內成像模擬；光阻模型；擴散；遠距照護；藍芽；心電圖

並列摘要

This thesis is divided into two parts; the first part is microlithography simulation. This part is focused on the simulation of the optical system throughout the whole microlithography process, including the photochemical reaction, the establishment of photoresist and the effect of diffusion. As the VLSI manufacture technology improves, the feature size of micro-electronic devices become much smaller than the wavelength of the exposure light source, and the limits of micro-lithography image system is continuously challenged. The exposed image results clearly deviate from the original design pattern due to the optical diffraction effect. Furthermore, a deeper discussion about the modeling of photoresist is proposed. The Dill model, Kim model, original Mack model and enhanced kinetic model are introduced and compared. We discuss the effect that the standing wave of light brings on to the design pattern, and use numerical methods to simulate acid diffusion effect. The chemical amplification resist used in lithography process enhances the lithography reaction with the low-energy lights. Moreover, the reaction of the lights can be regarded as the acid diffusion phenomena. We can look thoroughly into the acid diffusion equation by using matrix algebra and specific algorithms. However the non-numerical solution can be obtained by fast Fourier transform method FFT. At last, we expect to develop a fast and reliable auxiliary system for lithography simulation. The other topic is about Physiological signals transmission and analysis. The importance of Telecare is emphasized as the technology improves and the amount of medical resources becomes abundant. Therefore, the establishment of a platform to store personal health records for wireless biological measurement instruments and smart phone applications is necessary. Patients can upload the measured results by the biological instruments at home via Bluetooth or wireless network to a personal health care platform. Personal health records are available to them and their doctor through an internet access with intelligent mobile phones without restrictions of time or location. Our system can upload the measurements of blood pressure, as well as the results of ECG analysis. We utilize Support Vector Machines and MIT-BIH Arrhythmia database to complete the network back-end analysis systems. Patients can receive real-time ECG on the phone; in addition, we implemented a real-time algorithm in the smart phone which can diagnose heart diseases according to their health care records. In this case, both transfer and monitoring can be simply done.

並列關鍵字

Projection Optical Micro-lithography ； Resist Image Simulation ； Photoresist Model ； Diffusion Function ； Telecare ； Bluetooth ； Electrocardiography (ECG)

參考文獻

[2] Ming-Fong Tsai, "Abbe-PCA: Compact Abbe’s Kernel Generation for Micro lithography Aerial Image Simulation using Principal Components Analysis." Master Thesis, National Taiwan University, 2009.

[5] Dusit Thanapatay, Chaiwat Suwansaroj. "ECG beat classification method for ECG printout with Principle Components Analysis and Support Vector Machines." 2010 International Conference on Electronics and Information Engineering (ICEIE 2010).

[11] Wai Kei Lei, Ming Chui Dong, Jun Shi and Bin Bin Fu. “Automatic ECG Interpretation via Morphological Feature Extraction and SVM Inference Nets” Circuits and Systems, 2008. APCCAS 2008. IEEE Asia Pacific Conference on. 2008.

[12] S. Osowski, and T. H. Linh, “ECG beat recognition using fuzzy hybrid neural network,” IEEE Trans. Biomedical Engineering, vol. 48, no. 11, pp. 1265-1271, Nov. 2001.

[13] M. Engin, “ECG beat classification using neuro-fuzzy network,” Pattern Recogn. Letters, vol.25, pp.1715-1722, 2004.

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光蝕刻技術模擬以及生理訊號之傳輸與分析

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