博碩士論文 107323605 詳細資訊




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姓名 楊毓璞(Yu-Pu Yang)  查詢紙本館藏   畢業系所 機械工程學系
論文名稱 基於電漿發射光譜數據之人工神經網路輔助氮化鋁薄膜的應力分析與預測
(Artificial neural network assisted stress analysis and prediction of aluminum nitride thin films based on optical emission spectroscopy data)
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摘要(中) 在本研究中,我們提交了由光學發射光譜(optical emission spectroscopy, OES)收集的復雜的及時電漿數據。在不考慮複雜因素的情況下,從一組復雜的物理參數,如氮化鋁(Aluminum Nitride, AlN)薄膜的殘餘應力獲取了一系列解決方案。AlN具有較高的應力穩定性、熱穩定性和化學穩定性,我們採用脈沖直流電濺射法在矽基板上沉積AlN。我們想要知道的一個重要答案是,沈積的薄膜的應力是壓縮的還是拉伸的。為了回答這個問題,我們可以訪問任意多的光譜數據,記錄數據生成一個庫,並利用主成分分析(Principal Component Analysis, PCA)來降低復雜數據的復雜性。經過PCA預處理後,我們試圖證明我們是否可以採用標準的人工神經網路(Artificial Neural Network, ANN),以獲得一個足夠解析度的機器思維分類方法來區分AlN薄膜的應力類型。因此,通過這些機器學習練習,這些輔助分類可以擴展到未來其他感興趣的半導體研究。
摘要(英) In this study, we present complex real-time plasma data collected by optical emission spectroscopy (OES). A series of solutions were obtained from a set of complex physical parameters, such as the residual stress of Aluminum Nitride (AlN) films, without taking into account complex factors. AlN has high stress stability, thermal stability and chemical stability. AlN was deposited on silicon substrate by pulsed direct current sputtering. One of the key answers we want to know is whether the stresses on the deposited film are compressed or stretched. To answer this question, we can access as much spectral data as we want, record the data to generate a library, and use Principal Component Analysis (PCA) to reduce the complexity of complex data. After PCA pretreatment, we tried to prove whether we could use standard Artificial Neural networks (ANN) to obtain a machine-mind classification method with sufficient resolution to distinguish stress types in AlN films. Therefore, through these machine learning exercises, these auxiliary classifications can be extended to other interesting semiconductor research in the future.
關鍵字(中) ★ 機器學習
★ 主成份分析
★ 氮化鋁
★ 薄膜應力
★ 光放射光譜
關鍵字(英) ★ Machine Learning
★ Principal components analysis
★ Aluminum Nitride
★ Thin Film Stress
★ Optical Emission Spectroscopy
論文目次 中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 v
表目錄 vi
一、緒論 1
1-1 前言 1
1-2 研究背景 2
1-3 研究目的 3
二、研究理論 4
2-1 薄膜沈積原理 4
2-2 薄膜材料介紹 7
2-3 薄膜濺鍍方法 9
2-4 薄膜應力量測 12
2-5 機器學習 15
2-6 多層感知器 16
2-7 主成分分析法 18
三、研究內容與方法 21
3-1 實驗流程 21
3-2 薄膜成長 22
3-2-1 試片準備 22
3-2-2 製程準備 23
3-2-3 製程監測與數據收集 23
3-3 薄膜品質偵測與量測 25
3-3-1 電漿發射光譜 25
3-3-2 X射線繞射儀 25
3-3-3 掃描電子顯微鏡 27
3-3-4 穿透式電子顯微鏡 28
3-4 數據處理 30
3-4-1 數據類型與結構 30
3-4-2 數據預處理 30
3-4-3 利用主成份分析法進行數據處理 31
3-5 機器學習模型建立與預測 35
3-5-1 神經網路搭建 35
3-5-2 模型訓練 39
3-5-3 利用模型預測 45
四、實驗結果與討論 47
4-1 薄膜結晶與應力 47
4-2 薄膜微觀結構分析 50
4-3 神經網路測試與選擇 52
4-4 神經網路最佳化預測與驗證 58
五、結論與未來展望 61
參考文獻 62
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指導教授 利定東(Tomi T. Li) 審核日期 2021-7-7
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