增量學習用於工業4.0瑕疵檢測

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姓名

林宗澤(LIN,TSUNG-TSE) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

增量學習用於工業4.0瑕疵檢測

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摘要(中)

自工業 4.0 開始高科技計畫後，世界各國為了保持其優勢皆開始以智慧工廠為目標，
其中自動光學檢測的技術在半導體領域中扮演著重要的角色，自動光學檢測在業界已有
一定程度的發展，各廠商皆已發展出各自的架構，但現行自動光學檢測會因為外在因素
導致有誤判的行為，後續需要進行人工目檢的二次檢查，而在人工目檢時會倚賴製程人
員的專業知識判斷並受到主觀意識的影響，導致人工目檢沒有統一的檢測標準。
故本研究提出以現有架構為基準，以深度學習模型取代現有人工目檢的方式，達到
統一標準的檢測方式，降低對製程人員的依賴程度，且半導體工廠具備多條自動光學檢
測生產線，以傳統方式訓練的模型普遍具有災難性遺忘及模型更新速度耗時的問題，為
改善傳統模型的問題及符合半導體工廠的生態體系，本研究透過增量學習及深度學習模
型的結合提出 Adaptive Aggregation Networks on Gradient Episodic Memory 的系統架構，
以改善傳統模型的災難性遺忘及取代人工目檢為目的，達到提升產品的良率及減少時間
與人力的成本

摘要(英)

Since Industry 4.0 started its high-tech project, countries all over the world have started
to aim for smart factories in order to maintain their advantages, among which automatic optical
inspection plays an important role in the semiconductor field. However, the existing automatic
optical inspection may lead to misjudgment due to external factors, and the secondary
inspection by manual visual inspection is required.
Therefore, this study proposes to replace the existing manual visual inspection with a deep
learning model based on the existing framework to achieve a unified standard inspection
method and reduce the dependence on process personnel. In order to improve the problems of
traditional models and to meet the ecological system of semiconductor factories, this study
proposes a system architecture of Adaptive Aggregation Networks on Gradient Episodic
Memory by combining incremental learning and deep learning models to improve the
disastrous forgetfulness of traditional models and to replace manual visual inspection, so as to
improve the product yield and reduce the time and labor cost.

關鍵字(中)

★ 增量學習
★ 瑕疵檢測
★ 深度學習

關鍵字(英)

論文目次

中文摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究貢獻 4
1.4 研究流程與論文架構 5
第二章文獻探討 7
2.1 自動光學檢測 7
2.2 增量學習 8
第三章研究方法: 12
3.1 自適應聚合網路梯度情境記憶系統架構 12
3.2 資料前處理Data Preprocessing 13
3.3 Gradient Episodic Memory (GEM) 15
3.4 Adaptive Aggregation Networks (Ada-Aggregate) 18
3.5 研究方法總結 19
第四章實驗結果 20
4.1 實驗設計 20
4.1.1 實驗環境 20
4.1.2 訓練資料集 21
4.1.3 訓練模型 22
4.1.4 基準模型 23
4.1.5 評估指標 24
4.2 AA-GEM與基準模型效能比較 26
4.2.1 CIFAR-100 資料集 27
4.2.2 MNIST Permutations 資料集 28
4.2.3 MNIST Rotations 資料集 29
4.2.4 Bumping dataset 資料集 30
4.3 Epochs參數對AA-GEM及基準模型的影響 32
4.4 Memory sizes對AA-GEM與基準模型的影響 34
4.5 AA-GEM參數設定的影響實驗 36
4.6 實驗總結與討論 39
第五章結論 40
5.1 研究限制 40
5.2 研究總結 40
5.3 未來研究方向 41
參考文獻 42

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指導教授

陳以錚

審核日期

2022-7-4

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