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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁健芳 | zh_TW |
| dc.contributor.advisor | Chien-Fang Ding | en |
| dc.contributor.author | 楊翼 | zh_TW |
| dc.contributor.author | Yi Yang | en |
| dc.date.accessioned | 2025-08-21T16:50:16Z | - |
| dc.date.available | 2025-08-22 | - |
| dc.date.copyright | 2025-08-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-30 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99214 | - |
| dc.description.abstract | 隨著電動車與5G通訊需求增加，傳統矽材料在高頻高壓環境下已顯不足。碳化矽(Silicon carbide, SiC)具備寬能隙、高導熱與優異的熱穩定性，是第三代半導體的關鍵材料之一。然而，因其具備高硬度特性而難以加工，進而導致製程成本上升。為改善此問題，本研究採用奈秒級紅外光光纖雷射進行表面預處理，透過熱誘導氧化反應於表面形成軟化層，以降低材料表面硬度並提升後續加工性。相較於飛秒或皮秒雷射，所採用之光纖雷射成本低廉、維護簡便，其設備成本約低100至1000倍，大幅提升產業應用之可行性。研究中以有限元素分析模擬雷射移動熱源之熱場變化，並建構機器學習模型預測不同參數對移除深度與粗糙度之影響。結果顯示當雷射速度為1000 mm/s、重疊率為44%時，表面硬度由40.17 GPa 降至0.77 GPa，提升52.25%研磨效率，有效提升加工效率並降低耗材消耗。 | zh_TW |
| dc.description.abstract | With the growing demand for electric vehicles and 5G communications, conventional silicon materials are increasingly inadequate under high-frequency and high-voltage conditions. Silicon carbide (SiC), with its wide bandgap, high thermal conductivity, and excellent thermal stability, is a pivotal third-generation semiconductor material. Nevertheless, its high hardness imposes substantial challenges on wafer-level fabrication.
The present study applies a nanosecond infrared fiber laser for surface pretreatment.Thermal oxidation is induced to form a softened surface layer, reducing hardness and improving subsequent machinability. Compared to femtosecond or picosecond lasers, fiber lasers offer significantly lower equipment and maintenance costs, approximately 100 to 1000 times cheaper, enhancing industrial feasibility. Finite element analysis was performed to simulate the transient thermal fields of the laser moving heat source, while machine learning models were developed to predict the influence of processing parameters on material removal depth and surface roughness. When processed under optimized conditions, specifically at a scanning speed of 1000 mm/s and a 44% overlap, the surface hardness was reduced from 40.17 GPa to 0.77 GPa, improving grinding efficiency by 52.25%. The proposed method effectively enhances processing performance and reduces consumable wear, offering a promising solution for precision machining of hard materials. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-21T16:50:16Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-21T16:50:16Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iii 目次 iv 圖次 vii 表次 x 第一章 緒論 1 1.1 研究動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究目的 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 第二章 文獻回顧 5 2.1 碳化矽物理特性 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 晶體結構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 半導體研磨製程技術 . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 傳統研磨拋光 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1.1 研磨、研光、拋光 . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2 化學機械拋光 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2.1 單晶碳化矽在 CMP 中的去除機制 . . . . . . . . . . . . . . . 8 2.2.3 無磨料化學拋光技術 . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.4 電漿輔助拋光製程 . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 雷射輔助加工（Laser-assisted machining） . . . . . . . . . . . . . . 11 2.3.1 吸收率 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.2 雷射輔助拋光與材料移除機制 . . . . . . . . . . . . . . . . . . . 12 2.3.3 雷射與材料交互作用機制 . . . . . . . . . . . . . . . . . . . . . . 14 2.4 各類製程於碳化矽拋光應用之效能比較 . . . . . . . . . . . . . . . . 16 2.5 有限元素法於雷射溫度場分析之應用 . . . . . . . . . . . . . . . . . 17 2.5.1 雷射熱源建置 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.6 實驗設計方法 (Design of experiment, DOE) . . . . . . . . . . . . . . 17 2.6.1 類神經網路 (Artificial neural network, ANN) . . . . . . . . . . . . 18 2.6.1.1 RSM vs. ANN . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.6.2 機器學習演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6.3 決策樹 (Decision tree) . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6.3.1 隨機森林模型（Random forest） . . . . . . . . . . . . . . . . 21 2.6.3.2 梯度提升（Gradient boosting） . . . . . . . . . . . . . . . . . 21 2.6.3.3 極限梯度提升 (Extreme gradient boosting, XGBoost) . . . . . 22 2.6.4 機器學習性能評估指標 . . . . . . . . . . . . . . . . . . . . . . . 22 2.7 小結 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 第三章 研究方法 24 3.1 實驗流程 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2 材料製備 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3 實驗設備 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.4 實驗分析儀器 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4.1 雷射掃描共軛焦顯微鏡 . . . . . . . . . . . . . . . . . . . . . . . 28 3.4.2 機械表面測試平台 . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.3 掃描電子顯微鏡 . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4.4 能量散射 X 射線光譜 . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4.5 晶圓減薄研磨系統 . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.4.6 X 射線光電子能譜儀 . . . . . . . . . . . . . . . . . . . . . . . . . 34 第四章 結果與討論 35 4.1 雷射參數調控 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1.1 脈衝能量 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1.2 雷射掃描速度與掃描間距 . . . . . . . . . . . . . . . . . . . . . . 36 4.1.3 雷射加工次數 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2 溫度場分佈 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.3 各機器學習模型之效能比較 . . . . . . . . . . . . . . . . . . . . . . . 42 4.3.1 準確度分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.3.2 特徵重要性分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.4 材料表面特性 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.5 儀器驗證分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.5.1 X 射線光電子能譜分析 . . . . . . . . . . . . . . . . . . . . . . . 50 4.5.2 奈米壓痕硬度分析 . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.5.3 磨輪損耗分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 第五章 結論與未來展望 55 5.1 結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2 未來展望 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 參考文獻 57 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 表面改質 | zh_TW |
| dc.subject | 光纖雷射 | zh_TW |
| dc.subject | 雷射輔助半導體製程 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 碳化矽 | zh_TW |
| dc.subject | Machine learning | en |
| dc.subject | Surface modification | en |
| dc.subject | Laser-assisted semiconductor processing | en |
| dc.subject | Fiber laser | en |
| dc.subject | Silicon carbide | en |
| dc.title | 光纖雷射輔助碳化矽晶圓加速研磨製程之表面改質研究 | zh_TW |
| dc.title | Fiber Laser-assisted Accelerated Grinding Process for Silicon Carbide Wafers Surface Modification | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃振康;蔡劭璞;張天立;蔡曜陽 | zh_TW |
| dc.contributor.oralexamcommittee | Chen-Kang Huang;Shao-Pu Tsai;Tien-Li Chang;Yao-Yang Tsai | en |
| dc.subject.keyword | 碳化矽,光纖雷射,雷射輔助半導體製程,表面改質,機器學習, | zh_TW |
| dc.subject.keyword | Silicon carbide,Fiber laser,Laser-assisted semiconductor processing,Surface modification,Machine learning, | en |
| dc.relation.page | 62 | - |
| dc.identifier.doi | 10.6342/NTU202501288 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-01 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物機電工程學系 | - |
| dc.date.embargo-lift | 2030-06-23 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
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