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研究生(外文):Tu, Yen-Liang
論文名稱(外文):Intelligent Manufacturing of Dry Oxide MOSCAP Device by Using Ensemble Learning
指導教授(外文):Lin, Shih-Chun
口試委員(外文):Su, PinChen, Shih-WeiKao, Ming-HsuanLin, Shih-Chun
外文關鍵詞:MOS CapacitorIndustry 4.0Machine LearningMulti-Layer Perceptron(MLP)Ensemble Learning
  • 被引用被引用:2
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隨著半導體產業的逐年發展,根據摩爾定律,元件的尺寸以及關鍵維度 正逐年縮小中。元件縮小會導致其中的物理機制複雜化(例:短通道效應、熱載子效應……等)以及生產成本提高,也因此我們在設計元件時必須要考慮所有的參數因素,且參數之間很容易有取捨效應。模擬的部分,傳統的BSIM模型我們可以改變元件的幾何參數去模擬出元件或電路的特性曲線,但是隨著元件尺寸的縮小,傳統的BSIM模型無法表現出小元件特有的物理性質,進而產生出較多誤差。
According to the Moore’s law, with the development of the semiconductor industry year by year, the size of devices and critical dimensions (CD) are shrinking year by year. The shrinkage of components will lead to the complexity of the physical mechanism (ex. short channel effect, hot carrier effect... etc.) and the increase of production cost. Therefore, we must consider all parameter factors when designing devices, and the parameters are easy to have trade-offs. For the simulation, we can change the geometric parameters to simulate the characteristic curve of the device or the circuit in the traditional BSIM model. However, as the size of the device shrinks, the traditional BSIM model cannot show the unique physical properties of small components and will lead to more errors.
In this research, we will use Metal-Oxide-Semiconductor Capacitor (MOSCAP) and machine learning to create machine learning models to promote smart manufacturing of devices. MOSCAP devices are manufactured using TSRI machines. We will change the parameters of process variation and then use the measured MOSCAP C-V curves and ensemble learning as well as multi-layer perceptron(MLP) mostly used in machine learning to fit our C-V curves. After that, we can create process-aware machine learning model and replace the conventional BSIM model.
摘要 i
誌謝 iii
Content iv
Figure List vi
Table List x
Symbol List xi
Chapter 1 Introduction 1
1.1 Introduction to MOSCAP 1
1.2 Introduction to Machine Learning 4
1.3 Introduction to Industry 4.0 6
1.4 Introduction to Ensemble Learning 7
1.4.1 Bagging 7
1.4.2 Boosting 8
1.5 Literature Review 9
Chapter 2 Experiment Method 11
2.1 Experiment Equipment 11
2.1.1 Wet Bench 11
2.1.2 Horizontal Furnace 12
2.1.3 Spin Coating and Developing System 13
2.1.4 I-Line Stepper 15
2.1.5 Dry Etching Machine 15
2.1.6 PVD Machines 16
2.2 Experiment Process 18
2.3 Device Measurement 19
2.3.1 Parameter Variation 20
2.3.2 How the Parameters Affect C-V Curve? 22
Chapter 3 Results and Discussions 25
3.1 Sample Measurements and Comparisons 25
3.1.1 Clean Method Comparison 27
3.1.2 Top Electrode Machine Comparison 31
3.1.3 Analysis for Device Measurement 35
3.2 Quality of the Device 35
3.3 Model Fitting 39
Chapter 4 Conclusion and Future Works 55
References 56
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