博碩士論文 955401020 詳細資訊




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姓名 李東諺(Tung-Yen Li)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 LED模組及光學干涉技術於TFT-LCD系統之應用發展
(Development of LED Module and Optical Interference Technology for TFT-LCD Applications)
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摘要(中) TFTLCD顯示器為目前平面顯示器中的主流,TFTLCD顯示器主要是由液晶面板模組,背光模組以及電路驅動模組三個部份所構成。
本篇論文提供了TFTLCD組件中有關新型背光模組的設計以及液晶面板的檢驗方式。我們使用LED模組為背光光源同時搭配微反射結構和光學擴散元件進行新型直下式背光模組的設計。經過實際測量,我們的設計相對於2003年R.S.West的設計,在均勻度上我們增加了24%(由60%到84%),亮度上我們增加了23.29%(由10000nt到12329nt)。另一方面,本論文提出了應用光學干涉的方法搭配類神經網路及統計分類方式進行TFTLCD面板中有關Gap Mura Defect的檢驗分類。我們目前可以將固定膠過多,面板內有外來物以及固定膠內有纖維聚集這3種造成Gap Mura Defect的原因檢出。搭配使用Neural Network Classification方法,Mean Squared Error (MSE)經過反覆學習後可以降低至0.01以下。由此方式可以將製程中不良的面板提前檢出,減低製作不良品的機率,藉以提高產線的良率。
摘要(英) Recently, TFT-LCD is the most popular display application in the flat panel display application. The main parts in the TFT-LCD system includes: LCD panel module, backlight module and electronic driver module. This paper provides the novel design of direct-in backlight module of the TFT-LCD system and the inspection method for the gap mura defect of the LCD panel. We applied the LED light source, secondary light-guide component and micro reflector structure to design the novel backlight module. Compared with West’s work in 2003, the uniformity ratio for our new design shows an increase of 24%, from 60% to 84%, and the luminance a 23.29% improvement, from 10 000 nits to 12 329 nits. On the other hand, we combine the optical interference method and neural network classification to detect the gap mura defect of the LCD panel. Three kinds of mura defects including non-uniformity sealant panel; panel with foreign material and fiber-cluster panel are tested. After learning process of Neural Network Classification method, the Mean Squared Error could decrease less than 0.01. By this method, we could sort out the bad panel and increase the yield rate of the production line.
關鍵字(中) ★ 背光模組
★ 干涉光學
★ 類神經網路
★ 液晶面板
關鍵字(英) ★ backlight module
★ interference pattern
★ neural network
★ LCD panel
論文目次 Table of Contents
中文摘要……………………………………………………………………………..……I
英文摘要……………………………………………………………………………..……II
誌謝………………………………………………………………………………….……III
Table of Content………………………………….………………………………….……IV
List of figures………………………………….…………………………………………VII
List of tables…………………………………………..………………………………….X
Explanation of Symbols……………………………….………………………………....XI
Chapter 1 Overview of Thin Film Transistor Liquid Crystal Display………………1
1.1 Introduction to TFT-LCD System……………………………………………………1
1.2 Current status of TFT-LCD…………………………………………………………..3
1.2.1 The Status of Back Lighting Module of TFT LCD System…………..………3
1.2.2 The Status of TFT LCD Panel Inspection …………………………..………..5
1.3 Research Motivation and Contributions……………………………………..………6
1.3.1 Research of the LED Backlight Module by a Light Guide Component……6
1.3.2 Research of the Mura Inspection of TFT LCD Panel……………………..…7
1.4 Dissertation Outline……………………………………………………….………...8
Chapter 2 LED Backlight Module by a Lightguide- Component…………………..9
2.1 Abstract……………………………………………………………………………..9
2.2 Introduction…………………………………………………………………………10
2.3 Material and Method…………………………………………………….………….12
2.3.1 Preliminary Design………………………………………………………….12
2.3.1.1 Preliminary Design of the LED Backlight module…………………..12
2.3.1.2 Evaluation of the Simulated Results of the Backlight Module……….14
2.3.1.3 Thermal Design of the LED Backlight module……………….……..15
2.3.1.4 Preliminary Simulation of the Backlight Module……………….…..16
2.3.2 Advanced Design…………………………………………………….……..26
2.3.3 Measurement and Methodlogy……………………………………………..30
2.4 Results……………………………………………………………………………..32
2.5 Summary…………………………………………………………………………..34
Chapter 3 Detection of Gap Mura in TFT LCDs by the Interference
Pattern Method…………………………………………………….…….36
3.1 Abstract…………………………………………………………………….……..36
3.2 Introduction………………………………………………………………….……37
3.3 Materials and Methods……………………………………………………………42
3.3.1 Theory-Fringes of equal thickness…………………………………….…..42
3.3.2 Experimental Equipment………………………………………………….46
3.3.3 Image Processing Method…………………………………………..…….47
3.3.4 Experimental Methodology of Panel Inspection…………….…………..48
3.3.4.1 Definition of Cross Points……………………………….………49
3.3.4.2 Definition of Binary Classification Method……………….…….52
3.3.5 Neural Network Classification System ...………………………………54
3.3.5.1 Definition of the parameters……………………………….……54
3.3.5.2 Experimental Procedure of Neural Network Classification….…60
3.4 Results…………………………………………………………………………64
3.5 Summary………………………………………………………………………71
Chapter 4 Conclusion and Future Work………………………………………..73
4.1 Conclusion……………………………………………………………………..73
4.2 Future Work……………………………………………………………………74
Reference………………………………………………………………………….76
List of figures
Fig. 1.1. Schematic of a basic element of a LCD system………………………….……….1
Fig. 2.1. Structure of the direct illumination-type LED Backlight module……………...11
Fig. 2.2. Secondary lightguide component model……………………………………….12
Fig. 2.3. Illustration of the backlight module with four secondary
Lightguide components………………………………………………………...13
Fig. 2.4. Positions of points P1- P16……………………………………………………..15
Fig. 2.5. Simulation results for the flat reflector…………………………………………16
Fig. 2.6. Illustration of ray tracing with total reflection in the backlight module………..17
Fig. 2.7. Schematic representation of ray tracing with a tilted reflector…………………17
Fig. 2.8. Diagram of the secondary lightguide component…………………………….18
Fig. 2.9. Right-hand spherical coordinates……………………………………………….18
Fig. 2.10. Relation of P0, P1, P2 and P’……………………………………………………19
Fig.2.11. Illustration of ray tracing with total reflecting in the backlight module…………22
Fig.2.12. Schematic representation of the ray tracing with a tilted reflector.……….….….22
Fig. 2.13. Structures and locations of the Triangle Cylinder Structured Array
(units: mm)……………………………………………………………………..23
Fig. 2.14. Schematic representation of the six sections (sections N1-N6)…………..…….23
Fig. 2.15. Triangle Cylinder Structured Array simulation results:
(a) N (5,10,5,6,6,6); (b) N (5,10,5,6,8,6); (c) N (5,10,5,8,8,8)……………..….25
Fig. 2.16. Triangle Cylinder Structured Array polar candela plot: Blue
line-N (5,10,5,6,6,6); Red line-N (5,10,5,6,8,6); Black line-N (5,10,5,8,8,8)....26
Fig. 2.17. Structures and locations of the Tetrahedron Reflector Structured
Array (units: mm)………………………………………………………………27
Fig. 2.18. Tetrahedron Reflector Structured Array simulation results:
(a) N (5,10,5,6,6,6); (b) N (5,10,5,6,8,6); (c) N (5,10,5,8,8,8)………….………29
Fig. 2.19. Tetrahedron Reflector Structure Array polar candela plot: Blue
line-N (5,10,5,6,6,6); Red line-N (5,10,5,6,8,6); Black line-N (5,10,5,8,8,8)…29
Fig.2.20. Test platform for the backlight module………………………………………….30
Fig. 2.21. Simulation results of the uniformity ratio………………………………………33
Fig. 2.22. Simulation results of the angle of the luminous intensity………………………34
Fig. 3.1. Examples of mura in TFT LCDs…………………………………………………38
Fig. 3.2 Interference patterns observed in a 7 inch TFT LCD panel without Liquid
Crystal under a sodium lamp……………………………………………………..40
Fig. 3.3. Different sealant problems that can cause mura defects…………………………41
Fig. 3.4. Different sealant problems which can cause mura………………………………41
Fig. 3.5. Normal interference patterns of a TFT LCD under sodium light………………..43
Fig. 3.6. Abnormal interference patterns of a TFT LCD under sodium light……………..44
Fig. 3.7. Light path of point source S through planar parallel plates……………….…….44
Fig. 3.8. Schematic representation of the interference pattern analysis system.
An image catcher is used to capture the panel image.…………………………..47
Fig. 3.9. Image processing flow chart…………………………………………………….48
Fig. 3.10. Panel judging flow chart……………………………………………………….49
Fig. 3.11. (a) 5 cross points on the top and 5 cross points on the left side. (b) 7 cross
points on the top. (c) 35 cross points in the central area………………..……..52
Fig. 3.12. Neural Network configuration built with one input layer, one hidden layer,
and one output layer.............................................................................................56
Fig. 3.13. 16 panels prepared for this experiment, 10 panels prepared for learning and
training, and 6 panels are for testing…………………………………………..60
Fig. 3.14. Set of good panels without mura. (a) section No. 34, (b) section No. 180……61
Fig. 3.15. Set of bad panels with mura. (a) section No. 122, dense image
(b) section No. 42, star shape image……………………………….…………..62.
Fig. 3.16. The judgement results of each sections on the LCD panels…………………..63
Fig. 3.17 Test results based on two different criteria: (a) Criterion 1:
threshold = 130 points; (b) Criterion 2: threshold = 120 points………………..68
List of tables
Table 2.1 The relation of α,β, l , m , n and θ2……………………………………………21
Table 2.2 Simulated Results of the Triangle Cylinder Structure Array and Tetrahedron
Reflector Structure Array………………………….……….…………………29
Table 2.3 Candela Plot Results of The Triangle Cylinder Structure Array and
Tetrahedron Reflector Structure Array…………….……….…………………29
Table 2.4 Measurement Result of The R.S.West’s Work and our design (Original
design and Advance design with Triangle Cylinder Array)………………...…31
Table 3.1 The features of the interference fringes…………………………….……….. 59
Table 3.2 Definition of output vectors and mura status defects………………..……….60
Table 3.3 Cross Points of 15 Panels………………………………………….…………64
Table 3.4 Compare Results of 15 Panels…………………………………….………….65
Table 3.5 Statistical Results for A Binary Classification Test………………….……….66
Table 3.6 Statistical Results When Acceptability Criterion=130 Points………….…….66
Table 3.7 Statistical Results When Acceptability Criterion=120 Points………….…….67
Table 3.8 Training data for input and output vectors…………………….……………..70
Table 3.9 The result of the test set………………………………………………………70
參考文獻 Reference:
[1] P. Yeh and C. Gu Optics of Liquid Crystal Displays, 2nd ed., Wiley, 2010, ch1.
[2] J. H. Lee, D. N. Liu and S. T. Wu, Introduction to Flat Panel Displays, 1st ed., Wiley, 2008, ch1.
[3] M. Doshi, R. Zane, F. J. Azcondo, “Low Frequency Architecture for Multi-Lamp CCFL Systems With Capacitive Ignition”, Journal of Display Technology, vol. 5, pp. 152-161., 2009.
[4] C. F. Lin, C. C. Wu, P. H. Yang, and T. Y. Kuo, ”Application of Taguchi Method in Light-Emitting Diode Backlight Design for Wide Color Gamut Displays”, Journal of Display Technology, vol. 5, pp. 323-330., 2009.
[5] R. S. West, H. Konijn, W. S. Smitt, S. Kuppens, N. Pfeffer, Y. Martnov, T. Yagi, S. Eberle, G. Harbers, T. WeiTan, and C. E. Chan, “High Brightness Direct LED Backlight for LCD-TV”, SID Intl Symp. Digest Tech Papers Book II, pp.1262-1265., 2003.
[6] C. K. Chen, D. C. G. Peng, R. H. W. Lin, and S. W. S. Chi, “Optical lens design for LED backlights”, AU Optronics Corp. Hsinchu, Taiwan 300, R.O.C.
[7] C. P. Hung, W. S. Chen, J. H. Lin, and W. Y. Li, “Novel Design for LED Lens and Backlight System”, Chi-Mei Optoelectronics Corp., Tainan, Taiwan 741, R. O.C.
[8] E. Hecht, Optics, 4th ed., Addison-Wesley, 2002, ch5.
[9] W. J. Smith, Modern Optical Engineering, the design of optical system, 3rd ed., McGraw Hill, 2000, ch4.
[10] J. M. Geary, Introduction to Lens Design with practical Zemax examples, 1st ed, Willmann-Bell., 2002, ch3.
[11] R. S. West, H. Konijn, S. Kuppens, N. Pfeffer, Q. V. Vader, Y. Martnov, T. Heemstra, J. Sanders, T. Yagi, and G. Harbers, “LED backlight for large area LCD TV’s”, Proc Int Disp Workshops, vol.10, pp.657-660., 2003.
[12] R. S. Chang, J. Z. Tsai, T. Y. Li, and S. L. Liao, ”LED Backlight Module by Lightguide Diffusive Component”, IEEE/OSA Journal of Display Technology, vol. 8, pp. 79-86., 2012.
[13] H. L. Liao, ”Simulation Research of Luminance and Uniformity of a New-Type Lightguide-Diffusive Component”, M. S. Thesis, NCU, Taiwan, R.O.C., 2008.
[14] R. S. Chang, J. Z. Tsai, T. Y. Li, and T. C. Chuang, ”LED Backlight Module by Lightguide Diffusive Component with Tetrahedron Reflector Array”, IEEE/OSA Journal of Display Technology, vol. 8, pp. 321-328., 2012.
[15] Manual, TracePro v4.1, Lambda Research Corporation. , 2007, ch9. (http://www.lambdares.com/)
[16] P. J. Ross: Taguchi Techniques fo r Quality .Engineering. 2nd ed, McGraw-Hill, New York, 1986, ch2.
[17] J. A. Freeman and D. M. Skupura: Neural Networks Algorithms, Applications and Programming Techniques. 1st ed, Adison-Wiley, Reading, MA, 1991, ch4.
[18] M. Gen and R. Cheng: Genetic Algorithm and Engineering Design. 1st ed, Wiley, New York, 1997, ch5.
[19] R. S. Chang, J. Z. Tsai, T. Y. Li, L. W. Ho and C. F. Yang,” Pretest gap mura on TFT LCDs using the interference pattern method”, IEEE/SICE International Symposium on System Integration, pp. 57-61., 2011.
[20] Y. X. Dai, Design and Operation of TFT-LCD Panels. 1st ed, Wunan Book Pub., Taiwan, R.O.C., 2006, ch5.
[21] C. H. Wen, “FPD Standard Activities in Taiwan: Measurements for mura and motion blur” International Meeting on Information Display & International Display Manufacturing Conference, 2006.
[22] H. W. Kao, J. C. Hung, and V. Hsu, “The Mura Graphics Problems in Large Area Photomask for Concept-Based Data Mining Techniques.” International Conference on Ubi-Media Computing, Taiwan, R.O.C., pp. 490-495., 2008.
[23] B. Xin, C. G. Zhuang, and H. Ding; “A New Mura Defect Inspection Way for TFT-LCD Using Level Set Method.” Signal Processing Letters, IEEE, vol. 16, No. 4, pp.: 311-314., 2009.
[24] Y. C. Lee, C. E. Shie, and D. C. Tseng, “LCD Mura Detection Based on Accumulated Differences and Multi-resolution Background Subtraction.” ICIG ’09. Fifth International Conference on Image and Graphic, pp.189-194., 2009
[25] H. C. Cheng, C. L. Ho, and W. C. Chen, and S. S. Yang. ”A Study of Process-Induced Deformations of Anisotropic Conductive Film (ACF) Assembly”. IEEE Trans. Components and Packaging Technologies, vol.29, p.p.577-588., 2006.
[26] K. Taniguchi; K. Ueta; and S. Tatsumi. “A Detection Method of Mura on A Coated Layer Using Interference Light.” IEEE International Conference of SMC, Taiwan, R.O.C., pp. 5047-5052., 2006.
[27] H. Koyama, F. Oohira, M. Hosogi, G. Hashiguchi, and T. Hamada. “Multiprobe SPM System Using Optical Interference Patterns.” IEEE Journal of Selected Topics in Quantum Electronics, vol. 13, No. 2, pp.415-422., 2007.
[28] J. Y. Park, S. H. Kim, W. G. Park, and Y. Kuk, "Interference Pattern of A Coherent Electron Beam by Localized Leakage Magnetic Field." Applied Physics Letters. vol. 78, No. 12, pp.1745-1747., 2001.
[29] Y. Zhang, J. Zhang, “A Fuzzy Neural Network Approach for Quantitative Evaluation of Mura in TFT-LCD.” International Conference on Neural Networks and Brain., pp. 424–427., 2005.
[30] H. D. Lin and C. H. Chien, “Automated Detection of Color Non-Uniformity Defects in TFT-LCD.” International Joint Conference on Neural Networks, pp.1405-1412., 2006.
[31] L. F. Chen, C. T. Su and M. H. Chen, “A Neural-Network Approach for Defect Recognition in TFT-LCD Photolithography Process.” IEEE Transactions on Electronics Packaging Manufacturing, vol 32, No. 1, pp.1-8., 2009.
[32] C. L. Chang, H. H. Chang and C. P. Hsu “An Intelligent Defect Inspection Technique for Color Filter.” IEEE International Conference on Mechatronics, pp.933-936., 2005.
[33] H. Hu, P. Y. Woo, "Fuzzy Supervisory Sliding-Mode and Neural-Network Control for Robotic Manipulators," IEEE Trans. on Industrial Electronics, vol. 53, No. 3, pp. 929- 940., 2006.
[34] M. Born and E. Wolf. Principle of Optics. 7th ed., UK, Cambridge Univ., 1999, ch 7.
[35] R. C. Gonzalez and R. E. Woods. Digital Image Processing. 2nd ed., Pearson/Prentice Hall, 2008, ch 3.
[36] R. J. Schalkoff. Artificial Neural Network. 2nd ed., McGraw-Hill, 1997, ch5.
[37] S. Haykin Neural Networks and Learning Machines. 3rd ed., Person/Prentice Hall, 2009, ch4
[38] C. S. Lin, R. S. Chang, “Sigital image processing for evaluating the characteristics of the microstructure of a holographic plate.” Optical and Lasers Technology, vol. 29, pp. 97–102., 1997
[39] G. C. Cawley, N. L. C. Talbot. “Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers.” Pattern Recognition, vol. 36, pp. 2585–2592., 2003.
[40] S. Tan. “Neighbor-weighted K-nearestneighbor for unbalanced text corpus.” Expert System with Applications, vol. 28, pp. 667–671., 2005.
[41] I. C. Chang, "Using Moire to study the shape and the shaking about the pain and the symmetry of the muscles", M.S. Thesis, NCU, Taiwan, R.O.C., 2005.
[42] S. L. Shih, "Touchless Fingerprint Sensor and Recognition System", M.S. Thesis, NTU, Taiwan, R.O.C., 2007.
[43] C. F. Yang, "The Rearch of Liquid Crystal Panel MURA", M.S. Thesis, NCU, Taiwan, R.O.C., 2011.
[44] T. Y. Li, J. Z. Tsai, R. S. Chang, L. W. Ho and C. F. Yang ” Pretest Gap Mura on TFT LCDs Using the Optical Interference Pattern Sensing Method and Neural Network Classification.” IEEE Transactions on Industrial Electronics, Accept for Publication, 2012.
[45] E. L. Lehmann, G. Casella, Theory of Point Estimation, 2nd ed., New York: Springer, 1998 ch5.
指導教授 蔡章仁、張榮森
(Jang-Zern Tsai、Rong-Seng Chang)
審核日期 2013-1-21
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