在矽晶圓加工過程中，晶棒(Ingot)經由切片(Slicing)與圓邊研磨(Edge-Grinding)後，需透過磨平(Lapping)製程研磨表面以去除切片時所造成的線鋸痕及表面破壞層。然而在大量生產過程中，會因許多因素導致晶圓在加工過程中產生破裂或碎邊，嚴重甚至會造成夾具(Carrier)產生形變，使晶圓在磨平過程中整盤的晶圓破裂，進而提高加工成本。
本研究透過裝設盤面上之加速規及置於研磨區附近之麥克風進行訊號擷取，並運用訊號分析在磨平製程中時域訊號及頻域訊號進行分段，分段後進行特徵擷取並運用主成分分析(Principal Component Analysis, PCA)進行降維並篩選新特徵，最終透過Hotelling’s T^2 value找出晶圓發生破裂的時間點；確認晶圓在研磨過程中發生破裂的第一時機，並初步探討研磨過程中造成破裂損傷的原因，將研磨晶圓因破裂造成的損失降至最低，達到利用感測器訊號偵測晶圓破片的目的。

In the process of silicon wafer processing, after the ingot is sliced and edge-grinded, the surface needs to be grinded through a Lapping process to remove the wire saw marks caused by the slicing process and surface damage layer. However, in the mass production process, due to many factors, the wafer will be cracked or chipped during the processing. In severe cases, it may even cause the carrier to be deformed, causing the entire wafer to break during the wafer flattening process. Further increase the processing cost.
In this research, the accelerometer on the panel and the microphone placed near the grinding area for signal data acquisition (DAQ), and signal analysis is used in the time domain and frequency domain signals are segmented to find out the different step parameters relationship, after doing the feature extractions of time domain and frequency domain feature, using the principal component analysis (PCA) to achieve dimension reduction and ranking the feature.
Finally, Hotelling’s T^2 value find out the time point when the wafer is cracked; confirm the first time when the wafer is cracked during the lapping process, and preliminary explore the reasons for the cracking damage during the polishing process, and the polished wafer is caused by the cracking.

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 x
符號說明 xi
第一章 緒論 1
1.1前言 1
1.2文獻回顧 3
1.2.1 晶圓加工損傷樣態 3
1.2.2 線上訊號檢測 6
1.2.3 晶圓加工訊號監測 9
1.3研究動機 11
1.4研究目標 14
第二章 研究相關基礎理論 15
2.1表面磨削加工 16
2.2晶圓表面研磨(Wafer Surface Grinding/ Surface Lapping) 18
2.3 雙面研磨機 19
2.3.1雙面研磨機運作方式 19
2.3.2雙面研磨機主要機件 20
2.3.3 太陽行星輪系 21
2.4訊號處理 22
2.4.1 奈奎斯特取樣定理(Nyquist Sampling Theorem) 22
2.4.2 傅立葉轉換(Fourier Transform) 23
2.5特徵抽取建立 26
2.5.1 時域特徵 26
2.5.2 頻域特徵 28
2.6機器學習 29
2.6.1 主成分分析(Principal Component Analysis, PCA) 29
2.6.2 Hotelling’s T2 value 34
第三章 實驗設計 36
3.1實驗設備 37
3.1.1加工機台 37
3.1.2破片樣態及工件 40
3.2量測系統 44
3.2.1感測器訊號擷取系統 44
3.2.2 PLC訊號擷取系統 46
3.2.3 擷取系統整合 48
3.3實驗規劃 51
第四章 結果與討論 56
4.1 感測器裝設位置 56
4.2 感測器之訊號頻段分析 61
4.3工況切割與訊號分析 68
4.4特徵擷取及特徵篩選 81
4.5學習模型管制圖建立 84
4.6管制圖判斷結果 87
第五章 結論 92
第六章 未來展望 94
6.1 破片樣態的分析與種類 94
6.2 管制圖線上監測 94
6.3 改善破片偵測量測系統 94
6.4 不同訊號分析手法 95
6.5 量化管制圖閥值 95
6.6 分析不同規格晶圓造成量測訊號之差異 95
參考文獻 96
附錄一、加速規規格表 99
附錄二、麥克風規格表 100
附錄三、訊號擷取卡規格表 101
附錄四、訊號擷取機箱規格表 102
附錄五、擷取電腦規格表 104
附錄六、夾具規格表 105
附錄七、雙面研磨機規格表 106

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