Defective Wafer Detection Using Sensed Numerical Features

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林靖宇(Ching-Yu Lin) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

(Defective Wafer Detection Using Sensed Numerical Features)

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摘要(中)

半導體製造的基本過程之一是切片，這意味著將晶棒切成許多晶片。
在切片過程中，可能會產生有缺陷的晶圓。
不幸的是，識別缺陷晶片的檢查既浪費時間又檢查困難。為了解決這個問
題，我們建立了一個系統，該系統在切片及表面檢查過程中會使用傳感器
收集晶圓特性（例如溫度，厚度，晶片表面上的圖案等）以檢測晶片在製
造過程中是否有缺陷。
我們在此系統中應用兩種不同模型-GRU 神經網絡和XGBoost。此兩種模型
經過微調後，根據實際數據分析的實驗結果顯示在晶圓的缺陷檢測方面，
GRU 神經網絡的預測準確度和模型訓練時間均優於XGBoost

摘要(英)

One of the fundamental processes in semiconductor manufacturing is slicing,
which means cutting an ingot into many wafers. During the slicing process, it
is possible to produce defective wafers. Unfortunately, the inspection to identify
defective wafers is time-consuming and dicult. To solve this problem, we build a
system, which uses sensors to collect features (e.g., temperature, thickness, pattern
on wafer surface, etc.) during the slicing process to detect if the wafers are defective
in the manufacturing process. Two dierent models, the GRU neural network and
XGBoost, are implemented in the proposed system. After ne-tuning both models,
experimental results based on real dataset indicate that the GRU neural network
outperforms XGBoost for wafer defective detection in both the prediction accuracy
and model training time.
ii

關鍵字(中)

★ GRU神經網絡
★ XGBoost
★ 深度學習
★ 缺陷晶圓檢測

關鍵字(英)

★ GRU neural networks
★ XGBoost
★ Deep learning
★ Defective wafer detection

論文目次

Contents
1 Introduction 1
2 RelatedWork 3
2.1 Unsupervised learning . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Supervised learning . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2.1 Image-based prediction . . . . . . . . . . . . . . . . . . 4
2.2.2 Value-based prediction . . . . . . . . . . . . . . . . . . 5
3 Preliminary 7
3.1 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.1.1 Supervised Learning . . . . . . . . . . . . . . . . . . . 7
3.1.2 Neural Networks . . . . . . . . . . . . . . . . . . . . . 9
3.1.3 Deep Neural Networks . . . . . . . . . . . . . . . . . . 9
3.1.4 Recurrent Neural Networks . . . . . . . . . . . . . . . 9
3.1.5 Long Short-Term Memory Cell . . . . . . . . . . . . . 10
3.1.6 Gated Recurrent Unit Cell . . . . . . . . . . . . . . . . 13
3.2 Overtting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2.1 Data Regularization . . . . . . . . . . . . . . . . . . . 14
3.2.2 Early stopping . . . . . . . . . . . . . . . . . . . . . . 15
3.2.3 Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Design 17
4.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.2 Internal Calculation . . . . . . . . . . . . . . . . . . . . . . . . 20
4.3 Defective Wafer Detection . . . . . . . . . . . . . . . . . . . . 20
4.3.1 Feature scaling . . . . . . . . . . . . . . . . . . . . . . 21
4.3.2 RNN model . . . . . . . . . . . . . . . . . . . . . . . . 22
5 Performance 24
5.1 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . 24
5.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 25
5.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 26
6 Conclusion and Future Work 31

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指導教授

孫敏德(Ming-Te Sun)

審核日期

2020-7-30

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