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研究生： Anifatul Faricha
Anifatul - Faricha
論文名稱： Diffraction Based Overlay Target Design for On-Product Overlay Measurement
Diffraction Based Overlay Target Design for On-Product Overlay Measurement
指導教授： Hung-Fei Kuo
Hung-Fei Kuo
口試委員: Chunliang.kuo
chunliang.kuo
Jau-Horng Chen
Jau-Horng Chen
學位類別： 碩士
Master
系所名稱： 工程學院 - 自動化及控制研究所
Graduate Institute of Automation and Control
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 78
中文關鍵詞： 光罩圖案潛像(AerialImage) 、Artificialneuralnetwork(ANN) 、diffractionbasedoverlay(DBO) 、光學散射儀 、解析度增強技術(RET) 、側壁角度(SWA).
外文關鍵詞： Aerial image, Artificial neural network (ANN), diffraction based overlay (DBO), optical scatterometry, resolution enhancement technique (RET), sidewall angle (SWA).
相關次數： 點閱：648  下載：4 
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    • To acquire the advanced-node technology, overlay becomes the key challenges for the nano-lithography in the semiconductor manufacturing. Overlay needs to evolve to accurately represent product device patterns for the control of overlay errors. The diffraction based overlay (DBO) is considered as the promising method, it is fast, non-destructive, stable, and also well-suited to being integrated with a 193-immersion lithography system for controlling the lithography process variations from die-to-die, wafer-to-wafer, and lot-to-lot. The DBO target is the candidate for the on-product overlay control since the DBO’s accuracy and resolution are beyond the diffraction limit. The calculation is more complicated and involved many sophisticated overlay target designs which should be robust against symmetry and asymmetry variation. Several resolution enhancement techniques (RET) such as sub-resolution assist feature (SRAF) and segmentation, are employed to meet the optimal overlay product. In this study, the segmented with SRAF model provided the sharpest aerial image contrast and the lowest critical (CD) error among other models. The feed-forward ANN model was integrated with the scatterometry to assess the resist CD profile in the DBO measurement. Based on the integrated ANN-scatterometry technique, the segmented with SRAF model had the closest result of resist CD profile to the device target among other models. The ANN-scatterometry technique, which was also used for predicting displacement offset of the DBO target with various SWAs, was successfully demonstrated. The numerical result of the minimum square error and the mean of residual in the flat region showed that the ANN model provided more effective displacement offset prediction than the linear and high order regression models, which implemented in the current scatterometry system.

    TABLE OF CONTENT ABSTRACT.......................................................................................................................................i ACKNOWLEDGMENT................................................................................................................iii TABLE OF CONTENT.................................................................................................................iv LIST OF FIGURE..........................................................................................................................vi LIST OF TABLE............................................................................................................................ix CHAPTER 1: INTRODUCTION 1.1 Background.......................................................................................................1 1.2 On-product Overlay Target Review..........................................................3 1.3 Motivation and Research Objectives.........................................................7 1.4 Thesis Organization........................................................................................7 CHAPTER 2: DIFFRACTION BASED OVERLAY MEASUREMENT 2.1 Introduction.......................................................................................................8 2.2 FDTD Lumerical for Grating Modeling...................................................9 2.3 Theory of DBO Measurements.................................................................10 2.4 DBO Target Design......................................................................................13 2.5 Summary..........................................................................................................15 CHAPTER 3: ARTIFICIAL NEURAL NETWORK FOR IMPROVING DBO ACCURACY 3.1 Introduction.....................................................................................................16 3.2 Overlay Target With Modeling of SWA................................................17 3.3 Feed-Forward ANN Model for DBO Estimation................................22 3.4 Comparison of ANN and Higher Order Model for DBO Measurement..................................................................................................27 3.5 Summary..........................................................................................................29 CHAPTER 4: DBO Target Design 4.1 Introduction.....................................................................................................31 4.2 The Optimal DBO Target Design..........................................................33 4.3 Aerial Image Analysis of DBO Target Design..................................36 4.4 ANN Model for On-product DBO Measurement..............................38 4.5 Comparison of ANN and Higher Order Model for DBO Target................................................................................................................49 4.6 Summary..........................................................................................................57 CHAPTER 5: CONCLUSION 5.1 Conclusion and Discussion........................................................................58 5.2 Contribution....................................................................................................59 5.3 Future Work....................................................................................................59 REFERENCES .............................................................................................................................60

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