Title

應用類神經網路預測LED元件之螢光粉比例

Translated Titles

A Study of Artificial Neural Network on the Phosphor Ratio Prediction of LED Components

Authors

鄭博仁

Key Words

LED、LED封裝、PLCC LED、類神經網路、倒傳遞類神經網路 ; LED package ; Super PCNeuron ; Back-Propagation Network ; Artificial Neural Network ; PLCC LED ; LED

PublicationName

中原大學工業與系統工程研究所學位論文

Volume or Term/Year and Month of Publication

2014年

Academic Degree Category

碩士

Advisor

黃博滄

Content Language

繁體中文

Chinese Abstract

LED製造流程一般分為上游磊晶、中游晶粒、下游封裝,而LED封裝廠為了取得符合客戶規格之CIE色座標,所以在點膠製程需要進行螢光粉試比例作業,來決定螢光粉比例值。本研究收集點膠製程相關資料,並將相關資料彙整統計分析,運用倒傳遞類神經網路建置PLCC LED封裝廠的核心技術之「螢光粉比例預測系統」,讓工作人員除了方便掌握各項資訊外,並運用這些歷史資料預先模擬適當的比例處方,不僅可以縮短螢光粉試比例所花費的時間也可以提升產出週期、節省人力、時間及相關資源成本,並可減少作業員因為反覆抄寫錯誤而導致產品良率降低。由實驗結果顯示,螢光粉試比例次數可以大幅度由每批平均4.5次降為1次,平均每批可節省78%試比例所花費的時間。而未來期望能藉由本研究建置之「試比例預測系統」,更進一步擴充到所有的LED封裝類產品,由此改善來提升產業之競爭力,進而使LED照明燈具可以加速平民化,落實節能減碳及無汙染照明之效益,降低溫室效應對生態之影響。

English Abstract

The production flow of LED can generally be separated to upstream epitaxy, midstream chip, and downstream packaging. In order to produce the right CIE coordinates for the needs of customers, LED packaging manufacturers need to test different phosphor to find out the best ratio of during the dispensing process. This study collects data from dispensing production process. A statistical analysis is applied to distinguish the important factors that would influence the ratio of phosphor. Finally, the BPN is deployed to construct the PRP system for LED components. This system not only helps the workers to easily handle the information, the capability of using historic data to simulate the most appropriate ratio also allows shortening the times spent on the tests. It also improves production cycle, man power, saves time and other related resources, and to avoid mistakes from human type error data which lead to low yield. The testing result of the system indicates that the average number of tests has been done from 4.5 to 1, saving 78% amount of time per lot compared to current situation. Through this study of PRP system for LED components, one expects to further apply the result to other packaging LEDs to enhance the competency of the industry and speed up the process of affordable LED lighting fixtures to everyone, that truly helps reducing our carton footprint, realize the benefits of zero-pollutant lighting, and bring down the greenhouse effect that has done to our eco planet.

Topic Category 電機資訊學院 > 工業與系統工程研究所
工程學 > 工程學總論
Reference
  1. 1. Chen, X., Zhao, J., Yu, L., Rong, C., Li, C., and Lian, S. (2011). A white light emitting phosphor Sr1.5Ca0.5SiO4:Eu3+, Tb3+, Eu2+ for LED-based near-UV chip: Preparation, characterization and luminescent mechanism. Journal of Luminescence, 131(12), 2697-2702.
    連結:
  2. 2. Chen, W.C., and Hsu, S.W. (2007). A neural-network approach for an automatic LED inspection system. Expert Systems with Applications, 33(2), 531-537.
    連結:
  3. 3. Chen, W.C., Lai, T.T., Wang, M.W., and Hung, H.W. (2011). An optimization system for LED lens design. Expert Systems with Applications, 38(9), 11976-11983.
    連結:
  4. 4. Chang, M., and Lin, P.P. (1999). On-line free form surface measurement via a fuzzy-logic controlled scanning probe. International Journal of Machine Tools and Manufacture, 39(4), 537-552.
    連結:
  5. 5. Cheng, H.C., Lin, J.Y., and Chen, W.H. (2012). On the thermal characterization of an RGB LED-based white light module. Applied Thermal Engineering, 38, 105-116.
    連結:
  6. 6. Dai, K., and Gao, X. (2013). Estimating antiwear properties of lubricant additives using a quantitative structure tribo-ability relationship model with back propagation neural network. Wear, 306(1–2), 242-247.
    連結:
  7. 7. Du, Y., Cai, Y., Chen, M., Xu, W., Yuan, H., and Li, T. (2014). A Novel Divide-and-Conquer Model for CPI Prediction Using ARIMA, Gray Model and BPNN. Procedia Computer Science, 31, 842-851.
    連結:
  8. 9. Hsieh, L.F., Hsieh, S.C., and Tai, P.H. (2011). Enhanced stock price variation prediction via DOE and BPNN-based optimization. Expert Systems with Applications, 38(11), 14178-14184.
    連結:
  9. 10. Lei, Z., Xia, G., Ting, L., Xiaoling, G., Qiao Ming, L., and Guangdi, S. (2007). Color rendering and luminous efficacy of trichromatic and tetrachromatic LED-based white LEDs. Microelectronics Journal, 38(1), 1-6.
    連結:
  10. 11. Lin, C.C. (2007). A Neural Network Approach on the Optimum Designed, National Pingtung University of Science and Technology.
    連結:
  11. 12. Mao, Z.Y., Zhu, Y.C., Gan, L., Zeng, Y., Xu, F.F., Wang, Y., Tian,H., Li, J., Wang, D.J. (2013). Tricolor emission Ca3Si2O7: Ln (LnCe, Tb, Eu) phosphors for near-UV white light-emitting-diode. Journal of Luminescence, 134, 148-153.
    連結:
  12. 13. Mashrei, M.A., Seracino, R., and Rahman, M.S. (2013). Application of artificial neural networks to predict the bond strength of FRP-to-concrete joints. Construction and Building Materials, 40, 812-821.
    連結:
  13. 14. Pandharipande, A., and Caicedo, D. (2011).Daylight integrated illumination control of LED systems based on enhanced presence sensing. Energy and Buildings, 43(4), 944-950.
    連結:
  14. 15. Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
    連結:
  15. 16. Srinivas, Y., Raj, A.S., Oliver, D.H., Muthuraj, D., and Chandrasekar, N. (2012). A robust behavior of feed forward back propagation algorithm of artificial neural networks in the application of vertical electrical sounding data inversion. Geoscience Frontiers, 3(5), 729-736.
    連結:
  16. 17. Wang, Z., and Tan, Y.K. (2013). Illumination control of LED systems based on neural network model and energy optimization algorithm. Energy and Buildings, 62, 514-521.
    連結:
  17. 18. Yadav, P.J., Joshi, C.P., and Moharil, S.V. (2013). Two phosphor converted white LED with improved CRI. Journal of Luminescence, 136, 1-4.
    連結:
  18. 19. Yeh, N., and Chung, J.P. (2009). High-brightness LEDs—Energy efficient lighting sources and their potential in indoor plant cultivation. Renewable and Sustainable Energy Reviews, 13(8), 2175-2180.
    連結:
  19. 20. Yu, H., Yu, X., Xu, X., Jiang, T., Yang, P., Jiao, Q., Zhou, D., and Qiu, J. (2014). Excitation band extended in CaYAl3O7: Tb3+ phosphor by Ce3+ co-doped for NUV light-emitting diodes. Optics Communications, 317, 78-82.
    連結:
  20. 21. Zhmakin, A.I. (2011). Enhancement of light extraction from light emitting diodes. Physics Reports, 498(4–5), 189-241.
    連結:
  21. 26. 田雲生(2013),綠能介紹與應用,臺中區農業改良場特刊,(116),237-241。
    連結:
  22. 27. 呂紹旭(2010),從「十城萬盞」到「五十城兩百萬盞」-中國LED路燈市場推廣現況-LED特殊照明應用,光連:光電產業與技術情報,(87),21-23。
    連結:
  23. 28. 呂紹旭(2012),LED產品發展趨勢,光連:光電產業與技術情報,(101),35-46。
    連結:
  24. 29. 呂紹旭(2013),全球LED照明應用市場分析,光連:光電產業與技術情報,(106),50-52。
    連結:
  25. 30. 周大為(2004),紫外光發光二極體及場發射顯示器用之螢光材料合成及特性分析,國立臺灣大學。
    連結:
  26. 31. 孫慶成、陳志宏(2011),LED的發展與照明技術應用趨勢,前瞻科技與管理,1(2),1-23。
    連結:
  27. 32. 張英隆(2012),兩岸LED照明產業與應用市場分析,淡江大學。
    連結:
  28. 33. 張嘉玲、陳永燊、魏逸葳、鄭雅庭、王文生(2013),以倒傳遞類神經網路探討水庫水質與集水區降雨之相關性,農業工程學報,59(4),56-66。
    連結:
  29. 35. 黃瑩發(2011),適用於肢體障礙者之電腦操作輔具,國立成功大學。
    連結:
  30. 36. 楊宗勳、許梓恂、陳建成、陳正岳(2005),白光LED的色彩特性調制,光學工程,(90),76-82。
    連結:
  31. 37. 葉菁菁(2013),LED封裝試比例回饋系統之設計與實作,國立交通大學。
    連結:
  32. 39. 劉子歆、黃泓瑜(2013),台灣發光二極體產業之發展歷程:國家創新系統之觀點。
    連結:
  33. 43. 蔡尚麟(2014),白光二極體之CIE座標模擬系統,國立虎尾科技大學。
    連結:
  34. 45. 鄭錦勝(2011),LED封裝點膠系統創新設計之研究,國立中央大學。
    連結:
  35. 46. 羅梅君、安振基(2012),照明用白光LED定量螢光光譜測試與白光色彩結構在LED封裝工業上的應用,中華印刷科技年報,396-404。
    連結:
  36. 47. 台灣LED產業現況與發展建議-國家政策研究基金會(2013)。
    連結:
  37. 參考文獻
  38. 8. ENERGY STAR® Program Requirements for Solid State Lighting Luminaires, Eligibility Criteria, Version 1.1 (2008),EPA.
  39. 22. http://www.npf.org.tw/post/2/13038/. (2014.5.20查詢)
  40. 23. http://www.moneydj.com/. (2014.5.20查詢)
  41. 24. http://zh.wikipedia.org/wiki/CIE1931. (2014.5.20查詢)
  42. 25. 王進德、蕭大全(1994),類神經網路與模糊控制理論入門,全華科技圖書股份有限公司。
  43. 34. 張斐章、張麗秋(2010),類神經網路導論-原理與應用,滄海書局。
  44. 38. 葉怡成(2001),應用類神經網路,儒林圖書有限公司。
  45. 40. 劉偉仁、姚中業、黃健豪、鍾淑茹、金風(2014),LED螢光粉技術,五南出版社。
  46. 42. 陳登銘(2008),新電子白光LED螢光粉技術,三強鼎立。
  47. 44. 黃海靜、陳綱、呂貽標(2005),小議城市綠色照明規劃設計,土木建築與環境工程,27(5), 10-12。
  48. 48. 經濟部能源局,(2012),能源發展綱領,1-7。
  49. 49. 經濟部能源局,(2009),綠色能源產業旭升方案,1-10。