研究生: |
洪司融 Shih-Jung Hung |
---|---|
論文名稱: |
掃描探針微影製程參數最佳化之研究 Research on the Optimization of the Scanning Probe Lithography Processing Parameters |
指導教授: |
郭中豐
Chung-Feng Jeffrey Kuo |
口試委員: |
向四海
Su-Hai Hsiang 蘇德利 Te-Li Su 黃昌群 Chang-Chiun Huang 陳耿明 Geng Ming Chen |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 材料科學與工程系 Department of Materials Science and Engineering |
論文出版年: | 2009 |
畢業學年度: | 97 |
語文別: | 中文 |
論文頁數: | 108 |
中文關鍵詞: | 掃描探針微影術 、原子力顯微鏡 、陽極氧化 、田口方法 、灰關聯分析 、模糊推論 、倒傳遞類神經 、擬牛頓法 |
外文關鍵詞: | Scanning probe lithography, Atomic force microscope, Anodicoxidation, Taguchi method, Grey relational analysis, Fuzzy inference system, Back-propagation neural network, BFGS algorithm. |
相關次數: | 點閱:326 下載:0 |
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不論是筆記型電腦或數位相機等消費性電子產品在現代人的生活中皆是不可或缺的必需品,並往輕薄短小的目標邁進。因此,如何在矽晶圓上製作奈米尺寸之圖案,成為縮小電子元件之關鍵技術。
本論文提出的掃描探針微影術(Scanning probe lithography, SPL),是應用原子力顯微鏡(Atomic force microscope, AFM)的陽極氧化效應(Anodic oxidation),直接在矽晶圓表面上定義線寬,改良以往光學微影(Optical lithography)在光學繞射的困境,並利用田口實驗規劃控制因子及水準值,定出品質特性規範,再依照田口直交表(Orthogonal array)進行實驗。
由於田口方法(Taguchi method)是針對單一品質進行設計,就製程而言,往往無法達到整體品質特性之最佳化。因此,本論文結合灰關聯分析(Grey relational analysis) 和模糊推論系統(Fuzzy inference system)來整合多品質特性。利用灰關聯分析將數據正規化,並透過模糊推論系統(Fuzzy inference system)來整合多品質特性問題。並且利用田口方法的訊號雜訊比(Siganl to noise ratio, S/N)以及變異數分析(Analysis of variance)的結果進行探討,藉此找出對製程影響最大的顯著因子。最後利用倒傳遞類神經網路(Back-propagation neural network)結合擬牛頓法(BFGS)建構出預測製程的預測系統,以模擬實驗結果。
本論文經過多品質最佳化設計後,成功在矽晶圓上製作出寬度68奈米且均勻度良好的氧化矽線,將成為有利於縮小電子元件之技術。
Consumer electronics, ranging from notebooks to digital cameras, are considered as necessities in modern life. The trend for these products to become smaller, thinner, and lighter due to consumer demand and convenience introduces key technological advancement to sketch a pattern in nano-scale on silicon wafer surface.
The scanning probe lithography mentioned in this study used the anodic oxidation effect of the atomic force microscope to define the line width on the silicon wafer surface to resolve the problem of optical diffraction. We also utilized the Taguchi method to obtain factors and quality value to ensure quality characteristics followed by experimentation with orthogonal array.
Since Taguchi Method was initially designed for single quality characteristic, the method could not allow us to obtain multi-quality characteristics. We therefore combined grey relational analysis and fuzzy inference system for multi-quality characteristics. We used the grey relational analysis to normalize the experimental data and the fuzzy inference system to acquire multi-quality characteristics. From analyzing the signal-to-noise ratio and variance, we noted significant factors that affected the lithography process. Lastly, a prediction system was established to simulate the results by using back-propagation neural network with BFGS algorithm.
We had successfully produced evenly distributed silicon oxide lines of a 68nm width on the silicon wafer surface. This study can contribute to current techniques in producing desirable electronics for the market.
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