研究生: |
邱靖惠 Ching-Hui Chiou |
---|---|
論文名稱: |
整合文字探勘和本體論 於碳化矽晶圓化學機械拋光製程 專利分析研究 Integration with Text-Mining and Ontology-Based Patent Analysis on Chemical Mechanical Polishing of Silicon Carbide Wafers |
指導教授: |
陳炤彰
Chao-Chang Chen |
口試委員: |
管中徽
Chung-Huei Kuan 林榮慶 Zone-Ching Lin 張瑞芬 Jui-Fen Chang 林欽山 Sam Lin |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2018 |
畢業學年度: | 106 |
語文別: | 中文 |
論文頁數: | 163 |
中文關鍵詞: | 技術功效矩陣 、碳化矽晶圓 、化學機械拋光 、本體論 、皮爾森相關係數 |
外文關鍵詞: | Technique-function matrix, Silicon carbide wafer, Chemical mechanical polishing, Ontology, Pearson’s correlation coefficient |
相關次數: | 點閱:412 下載:3 |
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隨著電子電力系統需求的規格逐年提升,碳化矽(Silicon Carbide, SiC)被視為未來高功率元件的理想材料,但因SiC本身的高硬度及高抗化學性,使其在化學機械拋光(Chemical Mechanical Polishing, CMP)製程中面臨加工時間冗長、成本過高的技術問題,在製程研發上仍需注入龐大的研究資源以克服,而專利更是研發時的重要參考資料,但透過人力進行專利分析不僅耗時且費力,因此本研究旨在運用資訊科技以提升專利分析效率。本研究以Orbit檢索資料蒐集1986~2017年間,共837件英文的全球碳化矽晶圓之化學機械拋光專利家族資料,以R語言進行資料分析,分析方法以CMP本體論為基,進行專利分類、技術概念、功效分析。專利分類方面,利用本體論詞彙篩選出本體詞組,再另以皮爾森相關係數(Pearson’s correlation coefficient)擷取CMP本體論相關之文本詞組,根據技術類別進行專利分類。在技術概念與功效分析方面,並搭配了特定的選詞規則,篩選TF-IDF詞頻最高的詞組為結果。最後再自動建構出分類Road map圖及技術/功效矩陣圖呈現分析結果。於精確率評估結果顯示,專利分類為93.93%,技術概念為91.85%,功效分析為84.25%。
With urgent demand on electronic power system of high power performance, mono-crystalline Silicon carbide wafers have been considered as a high potential materials for high power IC devices. However, the ultra high hardness and excellent chemical stability of SiC wafers induce a very long processing time duration and high cost in chemical mechanical polishing (CMP) process. Many researches have indicated some potential improvement of CMP process of SiC wafers, a roadmap of future development is definitely needed to achieve above challenges. Patent deployment plays an important role in research resource, but it is inefficiently if all operations depend on manual work. This study aims to use text mining and ontology-based technology to enhance the efficiency of patent analysis. This study has been searched through Orbit patent database and collect 837 global patent families of SiC wafer chemical mechanical polishing (SiC CMP) from 1986 to 2017 and also analyzed by R language programming. Pre-constructed ontology method is used for patent classification, technical concept and function analysis. In patent classification, ontology phrase is an index that used to classify various technical patents and also applying the Pearson's correlation coefficient to extract the CMP related ontology phrases. In technical concept and function analysis, phrases with highest TF-IDF frequency are selected as results which based on specific rule of key phrases selecting. Results of this study can develop and obtain the classified road map and technique-function matrix. Results of evaluation rate of patent classification precision is 93.93%, concept precision is 91.85% and function precision is 84.25%. Future study can focus on developing a smart patent analysis system.
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