手機上隱藏式指紋辨識設計

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：35

、訪客IP：18.226.93.207

姓名

林哲玄(Jhe-Syuan Lin) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

手機上隱藏式指紋辨識設計

相關論文

★ 白光LED於住宅照明之設計與應用	★ 超廣角車用鏡頭設計
★ 適用於色序式微型投影機之微透鏡陣列積分器光學系統研製	★ 發光二極體色溫控制技術及其於色序式微型投影機之應用
★ 光學變焦之軌跡優化控制	★ LED光源暨LED與太陽光混和照明於室內照明之模擬與分析
★ 利用光展量概念之微型投影機光學設計方法與實作	★ 光學顯微鏡之鏡頭設計
★ DLP微型投影系統之光路設計	★ 高效率藍光碟片讀取頭
★ 模組化雙波長光學讀寫頭的設計與光學讀寫頭應用在角度量測的研究	★ 數位相機之鏡頭設計
★ 單光電偵測器之複合式光學讀寫頭	★ 三百萬畫素二點七五倍光學變焦手機鏡頭設計
★ 稜鏡玻璃選取對色差的影響與校正	★ 彩色影印機鏡頭設計

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

本論文針對現今主流的兩種面板手機提出隱藏於屏幕下的指紋辨識光學設計，一個為適用於Liquid Crystal Display (LCD)屏幕下的指紋辨識，另一個為適用於Organic Light-Emitting Diode (OLED)屏幕下的超薄型指紋辨識。LCD屏幕本身不具透明特性，要讓其實現屏幕下指紋辨識的難度較高，因此我們提出一個設計方法將屏幕表面的保護玻璃(Cover Glass)作為導光板，當指紋按壓在玻璃表面時，指紋的脊會破壞導光板的全反射特性紀錄下指紋的紋路。另外，我們在保護玻璃的出入瞳處設計一個反射斜面，此斜面具有斜向投影放大與縮小的功能，讓外觀設計可以達到窄邊框(Bezel Less)需求，使屏佔比可達98%。最後將設計實現在LCD屏幕手機上並取得高對比度的指紋影像。在OLED屏幕下超薄型指紋辨識設計部分，我們提出一個利用光阻堆疊的方式來製作準直器取代傳統Through Silicon Via (TSV)的方式可以降低製作成本。另外，模組整體厚度小於0.6 mm的設計使其可以被放到屏幕與手機中框之間，讓電池的體積不在受到指紋辨識模組影響。最後，我們將此模組實作並組裝到OLED屏幕與手機中框內，利用OLED屏幕發光取得良好對比度的指紋影像。

摘要(英)

This thesis proposes two fingerprint recognition design which is hidden under the panel of mobile phone, one is suitable for Liquid Crystal Display (LCD) panel, and the other one is suitable for Organic Light-Emitting Diode (OLED) panel. LCD panel isn’t transparent, so it is difficult to realize fingerprint recognition under the panel. Therefore, we propose a design method to use the cover glass on the screen as the light guide plate. When the fingerprint is pressed on the glass surface, the ridge of fingerprint will Frustrated Total Internal Reflection (FTIR) and then record the fingerprint pattern. In addition, we designed a reflective surface at the entrance and exit pupil of cover glass. This reflective surface has the function of enlargement and reduction, so that the appearance can be bezel less. The screen ratio is achieved 98%. Finally, this design is implemented on LCD panel’s mobile phone to obtain a high-contrast fingerprint image. In the ultra-thin fingerprint recognition design under the OLED panel, we propose a design the uses photoresist stacking to make the collimator instead of the traditional Through Silicon Via (TSV) method, through this method can reduce the cost. Furthermore, the module thickness is less than 0.6 mm so that it can be place between the screen and the middle frame of the phone. This can provide an advantage that the size of battery is not affected by the fingerprint module. Finally, we implemented and assembled this module into the OLED panel and the middle frame of mobile phone, and used OLED panel to emit light to obtain fingerprint images with good contrast.

關鍵字(中)

★ 指紋辨識
★ 光學設計
★ 全內反射
★ 準直器

關鍵字(英)

★ Fingerprint Recognition
★ Optical Design
★ Total Internal Reflection
★ Collimator

論文目次

目錄
摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 xi
第一章緒論 1
1-1 研究動機 1
1-2 文獻回顧 2
1-2-1 電容式指紋辨識 4
1-2-2 虹膜辨識 7
1-2-3 人臉辨識 9
1-2-4 超聲波指紋辨識 12
1-2-5 光學指紋辨識 13
第二章理論 17
2-1 解析度 17
2-2 光路展開圖法Tunnel Diagram 18
2-3 光展量Etendue 18
2-3-1 光展量Etendue定義 18
2-4 立體角與光展量(Etendue)關係 21
2-4-1 圓立體角之光展量(Etendue)計算 22
2-4-2 橢圓立體角之光展量計算 23
2-4-3 矩形立體角之光展量計算 24
2-5 對比度 25
2-6 均勻度 26
第三章 LCD屏幕下指紋辨識設計 28
3-1 設計導論 28
3-2 設計原理 28
3-3 設計規格 33
3-4 模擬與分析 36
3-5 LCD屏幕下指紋辨識實作 42
3-5-1 光源模組 42
3-5-2 保護玻璃 44
3-5-3 接收端模組 48
第四章超薄型OLED屏幕下指紋辨識 61
4-1 設計導論 61
4-2 設計原理 61
4-3 設計規格 64
4-4 模擬與分析 69
4-5 超薄OLED屏幕下指紋辨識實作 77
4-5-1 多層Collimator製作 77
4-5-2 Collimator貼合 78
4-5-3 模組堆疊 79
4-5-4 影像結果 81
第五章總結與未來展望 85
5-1 LCD屏幕下指紋辨識設計成果 85
5-2 OLED超薄屏幕下指紋辨識設計成果 86
5-3 設計結果比較 86
5-4 未來展望 87
參考文獻 90

參考文獻

1. https://zh.wikipedia.org/wiki/IPhone_5s
2. https://zh.wikipedia.org/wiki/%E4%B8%89%E6%98%9FGalaxy_Note_7
3. https://zh.wikipedia.org/wiki/%E4%B8%89%E6%98%9FGalaxy_S8
4. https://zh.wikipedia.org/wiki/IPhone_X
5. https://baike.baidu.com/item/vivo%20X21
6. https://zh.wikipedia.org/wiki/%E4%B8%89%E6%98%9FGalaxy_S10
7. https://baike.baidu.com/item/realme%20X50/51581265
8. 范聖武，「電容式指紋晶片之研究與設計」，國立交通大學，碩士論文，民國93年。
9. Satoshi Shigematsu, Hiroki Morimura, Yasuyuki Tanabe, Takuya Adachi and Katsuyuki Machida, “A single-chip fingerprint sensor and identifier”, IEEE Journal of Solid-State Circuits, Vol. 34, pp. 1852-1859, Dec. 1999.
10. Kwang-Hyun Lee and Euisik Yoon, “A 500 dpi capacitive-type CMOS fingerprint sensor with pixel-level adaptive image enhancement scheme”, 2002 IEEE International Solid-State Circuits Conference, San Francisco, CA, USA, Feb. 2002.
11. Yung-Shih Hsiung and Michael S.-C. Lu, “A CMOS capacitive pressure sensor chip for fingerprint detection”, 2011 16th International Solid-State Sensors, Actuators and Microsystems Conference, Beijing China, June 2011.
12. Seung-Min Jung, Jin-Moon Nam, Dong-Hoon Yang and Moon-Key Lee, “A CMOS integrated capacitive fingerprint sensor with 32-bit RISC microcontroller”, IEEE Journal of Solid-State Circuits, Vol. 40, pp. 1745-1750, July 2005.
13. Jeong-Woo Lee, Dong-Jin Min, Jiyoun Kim and Wonchan Kim, “A 600-dpi capacitive fingerprint sensor chip and image-synthesis technique”, IEEE Journal of Solid-State Circuits, Vol. 34, pp. 469-475, Apr. 1999.
14. Hiroki Morimura, Satoshi Shigematsu and Katsuyuki Machida, “A high-resolution capacitive fingerprint sensing scheme with charge-transfer technique and automatic contrast emphasis”, 1999 Symposium on VLSI Circuits. Digest of Papers, Kyoto Japan, August 2002.
15. Ye-Hsuan Yan, Chung-Chih Hung and Colman Huang, “Characteristic Comparison between Passive and Active Capacitive Fingerprint Sensors”, 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), Taoyuan Taiwan, Nov. 2020.
16. Marco Tartagni and Roberto Guerrieri, “A 390 dpi live fingerprint imager based on feedback capacitive sensing scheme”, 1997 IEEE International Solids-State Circuits Conference. Digest of Technical Papers, San Francisco, CA, USA, August 2002.
17. Yun Sang Kwak, Won Young Choi and Kwan Kyu Park, “Fingerprint imaging using CMUT impediography with glass waveguide”, 2017 IEEE International Ultrasonics Symposium (IUS), Washington, DC, USA, Nov. 2017.
18. Thomas Suwald and Thomas Rottschäfer, “Capacitive Fingerprint Sensor for Contactless Payment Cards”, 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Genoa Italy, January 2019.
19. Mukul Milind Ojha, Arun Kumar Anand, Gangaikondan S. Visweswaran and Dipankar Nagchoudhuri, “A relative comparative based datapath for increasing resolution in a capacitive fingerprint sensor chip”, Kolkata India, January 2005.
20. Ovidiu Vermesan, Knut H. Riisnaes, Laurent Le Pailleur, Jon B. Nysaether, Mark Bauge, Helge Rustad, Sigmund Clausen; Lars-Cyril Blystad, Hanne Grindvoll, Rune Pedersen, Robert Pezzani and David Kaire, “ A 500-dpi AC capacitive hybrid flip-chip CMOS ASIC/sensor module for fingerprint, navigation, and pointer detection with on-chip data processing”, IEEE Journal of Solid-State Circuits, Vol. 38, pp. 2288-2296, Dec. 2003.
21. Junichi Tabei, Hideaki Sasajima and Takeshi Mori, “Epoxy molding compound for fingerprint sensor”, 2016 International Conference on Electronics Packaging (ICEP), Hokkaido, Japan, April 2016.
22. Tom Tang, Kelly Chen, Kevin Tsai, Max Lu, Jensen Tsai and Yu-Po Wang, “High Dielectric Constant Molding Compounds for Fingerprint Sensor Packages”, 2018 IEEE 68th Electronic Components and Technology Conference (ECTC), San Diego CA, USA, June 2018.
23. Wang Dongdong, “Introduction of capacitive fingerprint sensor packaging technology”, 2017 18th International Conference on Electronic Packaging Technology (ICEPT), Harbin China, August 2017.
24. https://www.eet-china.com/mp/a1156.html
25. Al Bovik, The Essential Guide to Image Processing (Second Edition), Academic Press Inc., USA 2009.
26. Li Ma, Tieniu Tan, Yunhong Wang and Dexin Zhang, “Efficient iris recognition by characterizing key local variations”, IEEE Transactions on Image Processing, Vol. 13, pp. 739-750, June 2004.
27. John Daugman, “The importance of being random: statistical principles of iris recognition”, Pattern Recognition, Vol. 36, pp. 279-291, February 2003.
28. Kevin W. Bowyer and Mark J. Burge, Handbook of Iris Recognition, Springer London, Springer-Verlag London 2016.
29. Raida Hentati, Moncef Bousselmi and Mohamed Abid, “An embedded system for iris recognition”, 5th International Conference on Design & Technology of Integrated Systems in Nanoscale Era, Hammamet Tunisia, March 2010.
30. Eri Prasetyo Wibowo and Wisnu Sukma Maulana, “Real-Time Iris Recognition System Using a Proposed Method”, 2009 International Conference on Signal Processing Systems, Singapore, May 2009.
31. Niamh M. Fitzgerald, Christopher Dainty and Alexander V. Goncharov, “Extending the depth of field with chromatic aberration for dual-wavelength iris imaging”, Optics Express, Vol. 25, Issue 25, pp. 31696-31707, 2017.
32. Wenbo Dong, Zhenan Sun and Tieniu Tan,” A Design of Iris Recognition System at a Distance”, 2009 Chinese Conference on Pattern Recognition, Nanjing China, Nov. 2009.
33. Justin A. De Villar, Robert W. Ives and James R. Matey, “Design and implementation of a long range iris recognition system”, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, Pacific Grove CA USA, Nov. 2010.
34. Swati D. Shirke and C. Rajabhushnam,” Iris Recognition Using Visible Wavelength Light Source and Near Infrared Light Source Image Database: A Short Survey”, 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli India, April 2019.
35. Ken Hughes and Kevin W. Bowyer, “Detection of Contact-Lens-Based Iris Biometric Spoofs Using Stereo Imaging”, 2013 46th Hawaii International Conference on System Sciences, Wailea HI USA, Jan. 2013.
36. https://kknews.cc/tech/l6yjqpe.html
37. https://and1.tw/news/content/1172
38. Jinning Li, Yichen Zhou, Jie Ding, Cen Chen and Xulei Yang,” ID Preserving Face Super-Resolution Generative Adversarial Networks”, IEEE Access, Vol. 8, pp. 138373-138381, July 2020.
39. Recep Holat and Selman Kulaç, “ID identification by using face detection and recognition systems”, 2014 22nd Signal Processing and Communications Applications Conference (SIU), Trabzon Turkey, April 2014.
40. Daniel Sáez-Trigueros, Heinz Hertlein, Li Meng and Margaret Hartnett, “Shape and texture combined face recognition for detection of forged ID documents”, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija Croatia, June 2016.
41. Hwanjong Song, Sangyoun Lee, Jaihie Kim and Kwanghoon Sohn, “Three-dimensional sensor-based face recognition”, Applied Optics, Vol. 44, Issue 5, pp. 667-687, 2005.
42. Lihua Han, Quan Xiao and Shoujue Wang,” 3D face reconstruction with global and local constraints in double spaces”, 2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Nanjing China, Nov. 2016.
43. https://en.wikipedia.org/wiki/Facial_recognition_system
44. https://en.wikipedia.org/wiki/Face_ID
45. Ajmal Mian,” Illumination invariant recognition and 3D reconstruction of faces using desktop optics”, Optics Express, Vol.19, Issue 8, pp. 7491-7506, 2011.
46. Pei Zhou, Jiangping Zhu and Zhisheng You, “3-D face registration solution with speckle encoding based spatial-temporal logical correlation algorithm”, Optics Express, Vol. 27, Issue 15, pp. 21004-21019, 2019.
47. Jason Geng, “Structured-light 3D surface imaging: a tutorial” Advances in Optics and Photonics, Vol. 3, Issue 2, pp.128-160, 2011.
48. Song Zhang, “High-speed 3D shape measurement with structured light methods: A review”, Optics and Lasers in Engineering, Vol. 106, pp. 119-131, 2018.
49. Andreas Breitbarth, Timothy Schardt, Cosima Kind, Julia Brinkmann, Paul-Gerald Dittrich, Gunther Notni, “Measurement accuracy and dependence on external influences of the iPhone X TrueDepth sensor”, Proceedings Photonics and Education in Measurement Science 2019, Jena Germany, 2019.
50. https://www.imore.com/how-see-iphone-xs-dot-map
51. http://www.f4news.com/2017/09/20/yole-on-iphone-x-3d-innovations/
52. Seurin Jean-Francois, Zhou Delai, Xu Guoyang, Miglo Alexander, Li Daizong, Chen Tong, Guo Baiming and Ghosh Chuni, “High-efficiency VCSEL arrays for illumination and sensing in consumer applications”, Proceedings of the SPIE, Vol. 9766, San Francisco California United States, 2016.
53. Kazuyoshi Hirose, Hiroki Kamei, Takahiro Sugiyama and Yoshitaka Kurosaka, “200×200 µm2 structured light source”, Optics Express, Vol. 28, Issue 25, pp. 37307-37321, 2020.
54. https://www.systemplus.fr/reverse-costing-reports/apple-iphone-x-infrared-dot-projector/
55. https://zh.ifixit.com/Teardown/Face+ID+Teardown+-+Why+Face+ID+Not+Working+After+Repair/128388
56. https://www.systemplus.fr/reverse-costing-reports/stmicroelectronics-near-infrared-camera-sensor-in-the-apple-iphone-x/
57. Chang Peng, Mengyue Chen and Xiaoning Jiang, ” Under-Display Ultrasonic Fingerprint Recognition With Finger Vessel Imaging”, IEEE Sensors Journal, Vol. 21, Issue 6, pp. 7412-7419, 2021.
58. Hao-Yen Tang, Yipeng Lu, Xiaoyue Jiang, Eldwin J. Ng, Julius M. Tsai, David A. Horsley and Bernhard E. Boser, “3-D Ultrasonic Fingerprint Sensor-on-a-Chip”, IEEE Journal of Solid-State Circuits, Vol. 51, Issue 11, 2016.
59. Xiaoyue Jiang, Hao-Yen Tang, Yipeng Lu, Xi Li, Julius M. Tsai, Eldwin J. Ng, Michael J. Daneman, Martin Lim, Fari Assaderaghi, Bernhard E. Boser and David A. Horsley, “Monolithic 591×438 DPI ultrasonic fingerprint sensor”, 2016 IEEE 29th International Conference on Micro Electro Mechanical Systems (MEMS), Shanghai China, Jan. 2016.
60. Anna. Maeva and Fedar Severin, “High resolution ultrasonic method for 3D fingerprint recognizable characteristics in biometrics identification”, 2009 IEEE International Ultrasonics Symposium, Rome Italy, Sept. 2009.
61. Rainer M. Schmitt, W. Guy Scott, R.D. Irving, J. Arnold, C. Bardons, D. Halpert and L. Parker, “Ultrasonic imaging of fingerprints using acoustical impediography”, IEEE Ultrasonics Symposium, Montreal QC Canada, 2004.
62. John K. Schneider and Darold C. Wobschall, “Live scan fingerprint imagery using high resolution C-scan ultrasonography”, Proceedings. 25th Annual 1991 IEEE International Carnahan Conference on Security Technology, Taipei Taiwan, Oct. 1991.
63. Changting Xu, Yipeng Lu, Jessica Liu Strohmann and Hrishikesh Panchawagh, “Large Area Multi-Functional Ultrasound Sensor: Fingerprint, Touch Pressure, Passive Stylus”, 2020 IEEE International Ultrasonics Symposium (IUS), Las Vegas NV USA, Sept. 2020.
64. Ying Jie and Zheng Jihong, “Fingerprint sensor using a polymer dispersed liquid crystal holographic lens”, Applied Optics, Vol. 49, Issue 25, pp. 4763-4766, 2010.
65. Katsuki Tai, Masashi Kurita and Ichiro Fujieda, “Recognition of living fingers with a sensor based on scattered-light detection”, Applied Optics, Vol. 45, Issue 3, pp. 419-424, 2006.
66. Ichiro Fujieda and Hiroshi Haga, “Fingerprint input based on scattered-light detection”, Applied Optics, Vol. 36, Issue 35, pp. 9152-9156, 1997.
67. Bernard Geffroy, Philippe Le Roy, and Christophe Prat, “Organic light-emitting diode (OLED) technology: materials, devicesand display technologies” Polym. Int, Vol. 55, Issue 6, pp. 572–582, 2006.
68. Xuhui Peng and Yang Zeng, “Calibration algorithm for under-display optical fingerprint sensor”, Proc. SPIE 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging, Feb. 2019.
69. http://richard-rrb.blogspot.com/2019/05/fod-20195.html
70. Wei-Jei Peng, Yuan-Chieh Cheng, Jhe-Syuan Lin, Ming-Fu Chen and Wen-Shing Sun,” Development of miniature wide-angle lens for in-display fingerprint recognition”, Proc. SPIE 11231, Design and Quality for Biomedical Technologies XIII, Feb. 2020.
71. https://www.edmundoptics.com/knowledge-center/application-notes/optics/prism-tunnel-diagrams/
72. 廖竑偉，「小發散角的發光二極體照明器之設計」，國立交通大學，碩士論文，民國101年。
73. Virendra N. Mahajan, Optical Imaging and Aberrations: Part I. Ray Geometrical Optics, SPIE Press, Washington, 1998.
74. Robert W. Boyd, Radiometry and the Detection of Optical Radiation, John Wiley & Sons, New York, 1984.

指導教授

孫文信(Wen-Hsin Sun)

審核日期

2021-7-1

推文