臺灣博碩士論文加值系統

由於全球化的激烈競爭，為滿足各種客戶不同的需求，產品生命週期已變得越來越短，此種現象於近年來在電子產業尤甚。在如此高度競爭的產業中，如何藉由減少試產次數以縮減新產品開發的時程進而加速產品上市，已成為一個很重要的策略，但亦因此而衍伸出小樣本的學習問題。本論文針對一個存在於液晶面板製造廠的陣列(Array)製程之實務問題，在其曝光子製程中包含九個輸入屬性、七十二個輸出屬性，但資料僅有二十筆的個案進行研究。由於此種高輸入維度、多輸出、且少樣本的學習問題對於現有的機械學習演算法並不易處理，因此本研究應用盒鬚圖先行估算各屬性的合理值域後，再使用可能性評估機制來擴展樣本數量以建構穩健的管理模式。實驗結果顯示，藉由衍生出之虛擬樣本確可建構更穩健、精準的倒傳遞類神經網路，並祈能協助個案公司之工程師應用以預先推論相關的製程知識。

Owing to the global competition, product life cycles are becoming shorter and shorter to meet the various demands of customers, and such situation is usually seen in the electronic industry in recent years. In such highly competitive industry, it has become an important strategy to accelerate new products coming to the market by reducing the pilot runs. Therefore, it leads a small dataset learning problem. In this paper, a real learning task in the Array production process for a TFT-LCD (thin-film transistor liquid-crystal display) that has seventy-two output attributes is proposed. It is quite difficult for most existing modeling algorithms to deal with such a high dimensional situation when the sample size is small in the early stage of a manufacturing system. In order to archive it, this paper employs the box-and-whisker plots to estimate the possible distributions of attribute values, and uses a plausibility assessment mechanism to extend sample sizes for building robust management models. By deriving more samples, the results of the experiment show that the approach presented in this research is effective in building a more robust and precise back-propagation neural network. Engineers thus can infer more process knowledge in advance by applying the model.

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1 研究背景 1
1.1.1 個案描述 1
1.1.2 小樣本學習問題 4
1.2 研究動機 8
1.3 研究目的 9
1.4 研究範圍與限制 10
1.5 研究流程 10
第二章 文獻探討 12
2.1 資訊擴散技術為基礎的虛擬樣本產生法 12
2. 2 其他虛擬樣本產生法 19
第三章 研究方法 24
3.1 符號定義 24
3.2 虛擬樣本產生流程 25
3.2.1 值域推估 26
3.2.2 母體分配推論 28
3.2.3 虛擬值生成 29
3.2.4 樣本形成 30
3.3 倒傳遞類神經網路 30
第四章 實例驗證 37
4.1 實驗流程 37
4.1.1 實驗方式 38
4.1.2 預測誤差評估指標 38
4.1.3 假設檢定 39
4.2 個案實驗 40
4.3 實驗步驟詳述 44
4.4 實驗結果 50
第五章 結論與建議 54
5.1 結論 54
5.2 建議 54
參考文獻 56

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