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FUTURE OF OLEDs as LIGHTING SOLUTIONS & DISPLAYS

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STUDY ON OLED LIGHTS & DISPLAYS, FORECAST ON FUTURE OF OLEDs
METHODOLOGY: GENETIC ALGORITHM based
GREY MODELING

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FUTURE OF OLEDs as LIGHTING SOLUTIONS & DISPLAYS

  1. 1. FUTURE OF OLED AS LIGHTING SOLUTION 12th Nov, 2012 SHAMEER P.H. m.Tech - technology management
  2. 2. ABSTRACT In this market trend of using eco-friendly products, an exciting technology has been available in many small devices such as cell phones and digital camera displays for the last few years. It is claimed that this technology can cause a renaissance in the fields of lighting and display solutions. The technology is organic light emitting diode (OLED).
  3. 3. ABSTRACT  Objective:  Forecast the potential of OLEDs in the field of Lighting and Displays using Growth Curve models and GA based Grey Bernoulli Model.
  4. 4. “My interest is in the future, Because I’m going to spend the rest of my life there.” C.F. Kettering
  5. 5. TECHNOLOGY MANAGEMENT   TECHNOLOGY MANAGEMENT & TECHNOLOGY FORECASTING
  6. 6. TECHNOLOGY MANAGEMENT “TM is an interdisciplinary field concerned with the planning, development and implementation of technological capabilities to shape and accomplish the operational and strategic objectives of an organization” (National Research Council Report (USA), 1987)
  7. 7. TECHNOLOGY MANAGEMENT  Aims:  using technology as a source of competitive advantage.  Deals with:  Developing technology strategies  Developing/Acquiring technologies  Using Technologies.
  8. 8. TECHNOLOGY MANAGEMENT.. Industries seek to manage the technology they control, use or produce to contribute to corporate goals TODAY. They try to manage the development and implementation of technology to increase the realization of those goals TOMORROW. To manage, they draw on the lessons of YESTERDAY buttressed by management models developed from experience.
  9. 9. TM & TF In short, technology management draws on historical and future perspectives. Forecasting is intended to bring information to the technology management process by trying to predict possible future states of technology and/or conditions that affect its contribution to corporate goals.
  10. 10. TECHNOLOGY FORECASTING A tool for technology management..  WHAT?  “Prediction of the future characteristics of useful machines, procedures or techniques”  WHY? Many reasons, but mainly to Maximize the gain or minimize the loss from future conditions 
  11. 11. TECHNOLOGY FORECASTING  HOW?
  12. 12. Technology lifecycle The technology‟s performance improvement follows the S-Curve with 1) Embryonic Phase 2) Growth Phase 3) Maturity Phase 4) Saturation Phase 5) Declining Phase
  13. 13. FUTURE OF OLEDS AS LIGHTING SLOUTION 1. 2. 3. 4. OLED TECHNOLOGY- A REVIEW METHODOLOGY FORECASTING RESULTS AND DISCUSSIONS CONCLUSION
  14. 14. 1)OLED TECHNOLOGY- A REVIEW This Section deals with  Basics of Luminescence  Evolution and Types of Light Bulbs  OLED technology
  15. 15. Basics of Luminescence    Light is a form of Energy. To create light, another form of energy must be supplied There are two common ways for this to occur:  Incandescence  Luminescence
  16. 16. INCANDESCENT     LIGHT from HEAT. If you heat something to a high enough temperature, it will begin to glow. Sun and other Stars... Incandescent Bulbs, Halogen bulbs..
  17. 17. LUMINESCENCE     COOL LIGHT Caused by movement of electrons from more energetic state to less energetic state. Chemiluminescence, Electroluminescence, Bioluminescence…. Fluorescence &Phosphorescence
  18. 18. FLUORESCENCE  The luminescence caused by absorption of some form of radiant energy, and ceases as soon as the radiation causing it has stopped. PHOSPHORESCE NCE  The luminescence continues after the radiation causing it has stopped.
  19. 19. TYPES &EVOLUTION OF LAMPS
  20. 20. TYPES OF LAMPS  INCANDESCENT LAMPS  HALOGEN LAMPS  FLUORESCENT LAMPS  COMPACT FLLORESCENT LAMPS  HIGH INTENSITY DISCHARGE LAMPS  LOW PRESSURE SODIUM LAMPS  SOLID STATE LIGHTING
  21. 21. EVOLUTION OF LAMPS
  22. 22. IN TERMS OF LUMINOUS EFFICACY
  23. 23. OLED TECHNOLOGY DEALS WITH what is an OLED? structure of OLED, working principle of OLED its applications and advantages.
  24. 24. What is an OLED ?    OLEDs are energy conversion devices based on ELECTROLUMINESCENCE. OLEDs are organic because they are made from carbon and hydrogen. made by placing a series of organic thin films between two conductors.
  25. 25. background  The first observations of electroluminescence in organic materials were in the early 1950s by A. Bernanose and co-workers at the NancyUniversité, France.  M. Pope and co-workers discovered electroluminescence in organic semiconductors in 1963. Unfortunately, their high operating voltages (>1000V) prohibited them from becoming practical devices.
  26. 26. However, the scene changed when..  Chin Tang and Van Slyke introduced the first light emitting diodes from thin organic layers at Eastman Kodak in 1987.  In 1990 electroluminescence in polymers was discovered at Cavendish Laboratory, Cambridge University by Friend and coworkers.
  27. 27. then..  2000 - Alan G. MacDiarmid, Alan J. Heeger, and Hideki Shirakawa of University of Pennsylvania received Nobel Prize in chemistry for “The discovery and development of conductive organic polymer”.  1999- The First OLED display on market. 2008- The first OLED lighting fixture was introduced by OSRAM. 
  28. 28. STRUCTURE OF OLED OLED is a solid-state semiconductor device that is 100 to 500 nanometres thick or about 200 times smaller than a human hair. OLEDs can have either two layers or three layers of organic material.
  29. 29. structure  Substrate (clear plastic, glass, foil) The substrate supports the OLED.  Anode (transparent) - The anode removes electrons (adds electron "holes") when a current flows through the device.  Cathode (may or may not be transparent depending on the type of OLED) - The cathode injects electrons when a current flows through the device.
  30. 30. structure  Organic layers:  Conducting layer- made of organic plastic molecules that transport "holes" from the anode. One conducting polymer used in OLEDs is polyaniline.  Emissive layer - made of organic plastic molecules (different ones from the conducting layer) that transport electrons from the cathode; this is where light is made. One polymer used in the emissive layer is polyfluorene.
  31. 31. HOW IT WORKS..  The battery or power supply of the device containing the OLED applies a voltage across the OLED.  An electrical current flows from the cathode to the anode through the organic layers  At the boundary between the emissive and the conductive layers, electrons find electron holes.  The OLED emits light
  32. 32. TYPES OF OLEDS AMOLED PMOLED WHITE OLED   SMALL DISPLAYS  LARGE LIGHTING SOLUTIONS
  33. 33. APPLICATIONS
  34. 34. OLED DISPLAYS The essential requirements of present generation displays are reproduction of good light quality, brightness, contrast, improved colour variation, high resolution, low weight, reduction in thickness, reduction in cost, low power consumption. All these features can be seen in the OLED devices. OLEDs offer many advantages over both LCDs and LEDs
  35. 35. Applications       Televisions Cell Phone screens Watches Computer Screens Digital Camera Portable Device displays
  36. 36. OLED DISPLAYS  Thinner, lighter and flexible.
  37. 37. OLED DISPLAYS  BRIGHTER!!  The organic layers of an OLED are much thinner than the corresponding inorganic crystal layers of an LED.  Also, LEDs and LCDs require glass for support, and glass absorbs some light. OLEDs do not require glass.
  38. 38. OLED DISPLAYS  LARGE FIELD OF VIEW.
  39. 39. OLED DISPLAYS  FAST RESPONSE TIME OLED (10µs) LCD (200ms)
  40. 40. OLED lighting SOLUTIONS OLEDs are an entirely new way for architects, designers, system integrators, planners and luminaire makers to create with light. OLED devices are ultra-flat and emit very homogeneous light. The OLED grants a high degree of design freedom to users. By combining colour with shape OLEDs offer an exciting new way of decorating and personalizing surroundings with light.
  41. 41. applications    Mood Lighting Object Illumination General Illumination
  42. 42. advantages       Non-glaring area light source. High quality white light. (CRI 80) Requires less power (Low voltage DC(2-10 v)) Mercury free, RoHS conform. High luminous efficacy. Light weight (˜ 24 gm)
  43. 43. OLED AND OTHERS ….
  44. 44. WHO ALL ARE IN THE FIELD.. Source: HENDY
  45. 45. The key players       GE PHILIPS OSRAM KONICA MINOLTA MOSER BAER LUMIOTEC       VERBATIM OLED LEDON OLED PANASONIC IDEMITSU OLED LG CHEM. SMD NEC LIGHTING
  46. 46. WHY OLEDS…  Lighting Incandescent bulbs are inefficient !  Fluorescent bulbs give off ugly light !!  Ordinary LEDs are bright points; not versatile !!!   Displays: Significant advantages over liquid crystals Faster!  Brighter!!  Lower power!!!  OLEDs may be better on all counts 
  47. 47. 2)METHODOLOGY 1. DATA COLLECTION 2. FORECASTING BY NON-LINEAR REGRESSION. 3. FORECASTING BY GA BASED N GREY BERNOULLI METHOD. 4. RESULTS AND DISCUSSIONS 5. INFERENCE.
  48. 48. DATA COLLECTION Patent Data from LexisNexis Database is used to forecast.
  49. 49. PATENT DATA    A patent is an exclusive right to an invention over a limited period of within the country where the application is made. Patents are granted for inventions which are novel, inventive and have an industrial application. Patents measure inventive output and may be used as measure for innovation and the growth of that corresponding technology.
  50. 50. PATENTS & TLC     Patent growth generally follows a similar trend that can resemble S-Curve. In early stages of a technology the number of patents issued is very limited. A fast-growing period then follows when the number of patents filed and issued increases and then a plateau is reached. Because the patent process is costly and can take several years, filing a patent generally means there is optimism in economic or technical contribution.
  51. 51. PATENT DATA collection  The appropriate keywords were used to determine the number of patents for a given year globally.  The Scirus search tool was used to scan for the majority of world patents through the LexisNexis database*. (*LexisNexis patent database includes patents from the United States Patent and Trademark Office (USPTO), the European Patent Office (EPO), the Japanese Patent Office (JPO), and the Patent Cooperation Treaty (PCT) of the World Intellectual Property Organization (WIPO).)
  52. 52. forecasting using growth curve MODELS
  53. 53. forecasting using growth curves  Technology life cycles are used for modelling technological growth by using either Gompertz or logistics curves.  The methodology starts first with choosing between the logistic and Gompertz curves, and continues with forecasting different emerging technologies for the coming years. The lower asymptote is the starting level. The upper asymptote is the mature level. The point of inflexion is the point of maximum growth.
  54. 54. Growth Curves  Assumptions  The upper limit to the growth curve is known. The chosen growth curve to be fitted to the historical data is the correct one. The historical data gives the coefficients of the chosen growth curve formula correctly.    1) 2) The growth curves most frequently used by technological forecasters are the Pearl curve the Gompertz curve
  55. 55. Gompertz Model Logistic Model Where, ‘Yt’ is the measure of interest tagged by time ‘t’, ‘a’ is the Location Coefficient of the Curve, ‘b’ is the Shape Coefficient of the Curve and ‘L’ is the asymptotic maximum value of Yt Both the Gompertz curve and the logistic curve range from ‘zero’ to ‘L’ as ‘t’ varies
  56. 56. Finding the coefficients Gompertz Model Logistic Model Linear transformation of these equations using natural logarithm will lead to:
  57. 57. Selection between gompertz & pearl curves  The choice between these curves is performed by using a regression model (developed by P.H. Franses) that tests for non-linearity between the dependent variable (to be forecasted) and time.  As dependent variable, we will use the number of patents for the OLED technology under investigation
  58. 58. Selection between gompertz & pearl curves    The regression model for the Gompertz curve is linear in t and the expression for the logistic curve is nonlinear in t . Taking ∆ as the first difference operator, the regression model is represented as In the case when γ is significantly different from zero, the forecasting method to be used will be based on logistic curve rather than Gompertz curve.
  59. 59. GENETIC ALGORITHM BASED GREY MODELING Includes a) Grey Systems theory b) Non-linear Grey Bernoulli method c) Genetic Algorithm
  60. 60. Grey Systems theory     Introduced by Deng (1982). In systems theory, a system can be defined in terms of a color that represents the amount of clear information about that system. A system whose internal characteristics are unknown= a black box. If everything is clear= white system. Then, Grey System?
  61. 61. Grey modeling  Grey models require only a limited amount of data to estimate the behaviour of unknown systems.  Fundamental concepts of grey system theory  Grey system based prediction  Generations of grey sequences  GM(n,m) model  GM(1,1) model
  62. 62. Grey System based prediction  Grey models predict the future values of a time series based only on a set of the most recent data.  Assumptions    all data values to be used in grey models are positive The sampling frequency of the time series is fixed Can be viewed as curve fitting approaches.
  63. 63. Generation of grey sequences   Main task of GS theory is to extract the governing laws of the system. If the randomness of data is smoothed, the process will be easier.
  64. 64. GM(N,M) model GM(1,1) model    “Grey Model First Order One Variable”. „n‟ is the order of the difference equation and „m‟ is the number of variables.   The solution is an exponential curve. The model fails when there lies a saturation level for the data.
  65. 65. Non-linear Grey Bernoulli method   Developed by Liu, Dong, and Fang (2004). Model is,  Step 1: original data sequence,  Step 2: new sequence generated by AGO,
  66. 66. Non-linear Grey Bernoulli method  Step 3: The NGBM(1,1) model of the first-order differential equation  Fit the data in to the equation.  Find out the values of a and b using least square method.  Use genetic algorithm to improve the accuracy by optimizing the value of γ.
  67. 67.  Step 4 : Objective is to minimize the error function  The software Evolver 5.5 (Palisade) is used in this study to find the optimal value of „gamma‟ using Genetic Algorithm.  Step 5 : Substitute the values of a, b and γ into the following whitening equation
  68. 68.  Step 6 : Take the IAGO on , the corresponding IAGO is defined as where k = 2, 3, . . . , n.  This is our predicted value.
  69. 69. Genetic Algorithm John Holland, University of Michigan (1970‟s)
  70. 70. Biological evolution Organisms produce a number of offspring similar to themselves but can have variations due to:  Mutations   (random changes) Sexual Reproduction  (offspring have combinations of features inherited from each parent)
  71. 71.  Some offspring survive, and produce next generations, and some don‟t:  The organisms adapted to the environment better have higher chance to survive  Over time, the generations become more and more adapted because the fittest organisms survive
  72. 72. Genetic Algorithm  Genetic Algorithms are optimization techniques based on the mechanics of biological evolution.  A genetic algorithm maintains a population of candidate solutions for the problem at hand, and makes it evolve by iteratively applying a set of stochastic operators
  73. 73. Stochastic operators Selection replicates the most successful solutions found in a population at a rate proportional to their relative quality Recombination decomposes two distinct solutions and then randomly mixes their parts to form novel solutions Mutation randomly perturbs a candidate solution
  74. 74. softwares  IBM Inc.‟s SPSS  Microsoft Excel  Palisade Evolver
  75. 75. 3) FORECASTING A. B. OLED DISPLAY FORECAST OLED LIGHTING FORECAST
  76. 76. FORECASTING OLED DISPLAY TECHNOLOGY     PATENT DATA FORECAST RESULT ANALYSIS INFERENCE
  77. 77. PATENT DATA  Appropriate keywords were used to determine the number of patents on the OLED technology for a given year.  A 16 year span (1994– 2009) has been studied with this method
  78. 78. FORECAST East West North 90 PATTERN OBTAINED FROM NONLINEAR REGRESSIO N MODEL (LOGISTIC CURVE) FORECASTI NG 46.9 45.9 38.6 30.6 45 43.9 34.6 31.6 27.4 20.4 20.4 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
  79. 79. FORECAST PATTERN OBTAINED FROM GANGBM MODEL FORECASTI NG
  80. 80. WIDESCREEN PICTURES S-CURVE OF OLED DISPLAY TECHNOLOGY
  81. 81. PHASES OF LIFE EMEREGNT PHASE up to 2004 : GROWTH PHASE 2004-2015 : MATURITY PHASE 2015-2020 :
  82. 82. INFERENCE: R&D   The Technology is currently in its end of growth phase. Will enter its mature stage by 2015. MARKET     Uncertainty is reduced. High Competition . The mainstream technology in small screen displays. Best time for the Industry players to enter the market.
  83. 83. OLED LIGHTING TECHNOLOGY     PATENT DATA FORECAST RESULT ANALYSIS INFERENCE
  84. 84. PATENT DATA  Appropriate keywords were used to determine the number of patents on the OLED technology for a given year.  A 12 year span (1998– 2009) has been studied with this method
  85. 85. FORECAST East West North 90 PATTERN OBTAINED FROM NONLINEAR REGRESSIO N MODEL FORECASTI NG 46.9 45.9 38.6 30.6 45 43.9 34.6 31.6 27.4 20.4 20.4 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
  86. 86. FORECAST PATTERN OBTAINED FROM GANGBM MODEL FORECASTI NG
  87. 87. S-CURVE OF OLED LIGHTING TECHNOLOGY
  88. 88. PHASES OF LIFE EMEREGNT PHASE up to 2015 : GROWTH PHASE 2015-2025 : MATURITY PHASE 2025-2034 :
  89. 89. INFERENCE: R&D   OLED Lighting technology is still in its emergence phase. Huge investments are required. MARKET    Less Competition. High Opportunities. For Newcomers, this is the best (sometimes the only ) phase to enter the market.
  90. 90. CONCLUSIONS The OLED technology in Display Sector will enter its maturity stage by 2015. For small size displays OLED will be the mainstream technology. The competition will be in an increasing mode. For companies already present in the industry this may be a good phase to enter the market. But, for the newcomers it will be almost impossible.
  91. 91. CONCLUSIONS The OLED Lighting technology is still in its emerging phase. There is an uncertainty about the market. For newcomers this phase is often the only phase to enter the new market. The concerned industrial players can opt for investing in research and development activities. If the companies are still in confusion to invest in this field, its better for them to go for joint ventures.
  92. 92. References 1. J.P. Martino: A review of selected recent advances in technological forecasting, Technological Forecasting and Social Change 70 (2003); 719–733. 2. NPTEL course on Management Science 3. Franses, P.H.: A method to select between Gompertz and logistic trend curves, Technological Forecasting and Social Change 46 (1994) ;45-49 4. Li - Chang Hsu: A genetic algorithm based nonlinear grey Bernoulli model for output forecasting in integrated circuit industry, Expert Systems with Applications 37(2010); 4318—4323 5. D. E. Goldberg , Genetic Algorithm In Search, Optimization and Machine Learning, 1989 6. E. Kayacan, B. Ulutas , O. Kaynak: Grey system theory-based models in time series pre-diction, Expert Systems with Applications 37 (2010) ; 1784–1789 7. S.N.Sivanandam , S.N.Deepa: Introduction To Genetic Algorithms, Springer Publishing Company 2007 8. Genetic Algorithm ware house www.geneticalgorithms.ai-depot.com/Tutorial/Overview 9. 10. 11. obitco.com www.obitko.com/tutorials/genetic-algorithms/ www.geneticprogramming.com en.wikipedia.org/wiki/Genetic-algorithm
  93. 93. References 12. John K. Borchardt: Developments in organic displays; materials today September 2004. 13. HowStuffworks.com http://electronics.howstuffworks.com/oled.htm 14. novaled.com http://www.novaled.com/oleds/oled-in-lighting/ 15. 16. 17. Zill, D. G. and Cullen, M. R.:Advanced Engineering Mathematics 2nd ed. Massachusetts: Jones and Bartlett, 2000 Chun-I Chen, Pei-Han Hsin, Chin-Shun Wu: Forecasting Taiwan’s Major Stock Indices by the nash Nonlinear Grey Bernoulli Model; Expert Systems with Applications 37(2010); 7557–7562 US Department of Energy http://www.doe.gov/ 18. Display Search Report http://www.displaysearch.com/ 19. Cintelliq Report http://www.cintelliq.com/ 20. IEEE Spectrum on OLEDs http://spectrum.ieee.org/tag/OLED 21. Research and Market.com http://www.researchandmarkets.com/ 22. NanoMarket’s Report on OLEDs http://nanomarkets.net/ 23. http://www.oled-display.net/
  94. 94. References 24. 25. 26. 27. 28. 29. 30. http://ledsmagazine.com/ N. Thejo Kalyani , S.J. Dhoble: Organic light emitting diodes: Energy saving lighting technology—A review, Renewable and Sustainable Energy Reviews 16 (2012) 2696– 2723. About.com http://inventors.about.com/od/lstartinventions/a/lighting.htm megavolt.co.il http://www.megavolt.co.il/Tipsandinfo/types ofbulbs.html Tugrul U. Daim, Guillermo Rueda, Hilary Martin, Pisek Gerdsri:Forecasting emerging technologies: Use of bibliometrics and patent analysis,Technological Forecasting& Social Change 73 (2006) 981 – 1012. Yu-Heng Chen, Chia- Yon Chen, Shun- Chung Lee:Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies,international journal of hydrogen energy 36 (2011) 6957 - 6969. OLED Display.net Report on OLED Revenue http://www.oled-display.net/oled-revenuesforecast-to-reach-55b-by-2015-says- displaysearch/
  95. 95. QUESTIONS & COMMENTS

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