FUTURE OF OLED as LIGHTING SOLUTION

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Details on OLED technology. .Analyses the market trend on OLED Lighting and Displays and provides a forecast report. Genetic algorithm based Grey Modelling forecasting is used

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Future of oled as lighting solution: 

Future of oled as lighting solution SHAMEER P.H. m.Tech - technology management 12 th Nov, 2012

ABSTRACT: 

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).

ABSTRACT: 

ABSTRACT Objective: Forecast the potential of OLEDs in the field of Lighting and Displays using Growth Curve models and GA based Grey Bernoulli Model.

PowerPoint Presentation: 

“My interest is in the future, Because I’m going to spend the rest of my life there.” C.F. Kettering

TECHNOLOGY MANAGEMENT: 

TECHNOLOGY MANAGEMENT TECHNOLOGY MANAGEMENT & TECHNOLOGY FORECASTING

TECHNOLOGY MANAGEMENT: 

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 R eport (USA), 1987)

TECHNOLOGY MANAGEMENT: 

TECHNOLOGY MANAGEMENT Aims : using technology as a source of competitive advantage. Deals with : Developing technology strategies Developing/Acquiring technologies Using Technologies.

TECHNOLOGY MANAGEMENT..: 

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 .

TM & TF: 

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.

TECHNOLOGY FORECASTING: 

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

TECHNOLOGY FORECASTING: 

TECHNOLOGY FORECASTING HOW?

Technology lifecycle: 

Technology lifecycle The technology’s performance improvement follows the S-Curve with Embryonic Phase Growth Phase Maturity Phase Saturation Phase Declining Phase

FUTURE OF OLEDS AS LIGHTING SLOUTION: 

OLED TECHNOLOGY- A REVIEW METHODOLOGY FORECASTING RESULTS AND DISCUSSIONS CONCLUSION FUTURE OF OLED S AS LIGHTING SLOUTION

1)OLED TECHNOLOGY- A REVIEW: 

This Section deals with Basics of Luminescence Evolution and Types of Light Bulbs OLED technology 1) OLED TECHNOLOGY- A REVIEW

Basics of Luminescence: 

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

INCANDESCENT: 

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..

LUMINESCENCE: 

LUMINESCENCE COOL LIGHT Caused by movement of electrons from more energetic state to less energetic state. Chemiluminescence , Electroluminescence, Bioluminescence…. Fluorescence &Phosphorescence

PowerPoint Presentation: 

FLUORESCENCE PHOSPHORESCENCE T he luminescence caused by absorption of some form of radiant energy, and ceases as soon as the radiation causing it has stopped. The luminescence continues after the radiation causing it has stopped.

TYPES &EVOLUTION OF LAMPS: 

TYPES &EVOLUTION OF LAMPS

TYPES OF LAMPS: 

TYPES OF LAMPS INCANDESCENT LAMPS HALOGEN LAMPS FLUORESCENT LAMPS COMPACT FLLORESCENT LAMPS HIGH INTENSITY DISCHARGE LAMPS LOW PRESSURE SODIUM LAMPS SOLID STATE LIGHTING

EVOLUTION OF LAMPS: 

EVOLUTION OF LAMPS

PowerPoint Presentation: 

IN TERMS OF LUMINOUS EFFICACY

OLED TECHNOLOGY: 

DEALS WITH what is an OLED? structure of OLED, working principle of OLED its applications and advantages. OLED TECHNOLOGY

What is an OLED ?: 

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.

background: 

background The first observations of electroluminescence in organic materials were in the early 1950s by A. Bernanose and co-workers at the Nancy- Université , France. M. Pope and co-workers discovered electro-luminescence in organic semiconductors in 1963. Unfortunately, their high operating voltages (>1000V) prohibited them from becoming practical devices.

However, the scene changed when..: 

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 co-workers.

then..: 

t hen.. 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.

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 . STRUCTURE OF OLED

structure: 

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.

structure: 

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 .

HOW IT WORKS..: 

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

TYPES OF OLEDS: 

TYPES OF OLEDS SMALL DISPLAYS LARGE DISPLAYS PMOLED AMOLED WHITE OLED LIGHTING SOLUTIONS

APPLICATIONS: 

APPLICATIONS

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 OLED DISPLAYS

Applications : 

Applications Televisions Cell Phone screens Watches Computer Screens Digital Camera Portable Device displays

OLED DISPLAYS: 

OLED DISPLAYS Thinner , lighter and flexible.

OLED DISPLAYS: 

OLED DISPLAYS Brighter !! T he 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.

OLED DISPLAYS: 

OLED DISPLAYS Large field of view.

OLED DISPLAYS: 

OLED DISPLAYS FAST RESPONSE TIME LCD (200ms) OLED (10µs)

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. OLED lighting SOLUTIONS

applications: 

Mood Lighting Object Illumination General Illumination applications

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 ) advantages

PowerPoint Presentation: 

OLED AND OTHERS ….

There are some cons too.. : 

There are some cons too.. Very sensitive to water  Poor contrast in the sun light  Life time issues  Cost of production  The present research works are focused on these drawbacks.. 

PowerPoint Presentation: 

WHO ALL ARE IN THE FIELD.. Source: HENDY Consulting

The key players: 

The key players GE PHILIPS OSRAM KONICA MINOLTA MOSER BAER LUMIOTEC VERBATIM OLED LEDON OLED PANASONIC IDEMITSU OLED LG CHEM. SMD NEC LIGHTING

WHY OLEDs…: 

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 

2)METHODOLOGY : 

DATA COLLECTION FORECASTING BY NON-LINEAR REGRESSION. FORECASTING BY GA BASED N GREY BERNOULLI METHOD. RESULTS AND DISCUSSIONS INFERENCE. 2) METHODOLOGY

DATA COLLECTION: 

DATA COLLECTION Patent Data from LexisNexis Database is used to forecast.

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. PATENT DATA

PATENTS & TLC: 

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.

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 *. PATENT DATA collection (* 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).)

forecasting using growth curve MODELS: 

forecasting using growth curve MODELS

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. forecasting using growth curves The lower asymptote is the starting level. The upper asymptote is the mature level. The point of inflexion is the point of maximum growth.

Growth Curves: 

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. The growth curves most frequently used by technological forecasters are the Pearl curve the Gompertz curve

PowerPoint Presentation: 

Gompertz Model Logistic Model Where, ‘Y t ’ 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 Y t Both the Gompertz curve and the logistic curve range from ‘zero’ to ‘L’ as ‘t’ varies

SETTING THE UPPERLIMIT : 

The upper limit ‘L’ of each technology should be set based on the physical and the chemical limits which are imposed by nature. The Economic considerations should also be included. Usually, experts’ opinion is used. SETTING THE UPPERLIMIT

Finding the coefficients : 

Finding the coefficients Gompertz Model Logistic Model Linear transformation of these equations using natural logarithm will lead to:

Pearl vs. Gompertz Curves: 

Pearl vs. Gompertz Curves The slope of the Pearl curve involves y and (L-y), i.e., distance already come and distance yet to go to the upper limit. For large values of y, the slope of the Gompertz curve involves only (L-y), i.e., the Gompertz curve is a function only of distance to go to the upper limit.

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 Selection between gompertz & pearl curves

Selection between gompertz & pearl curves : 

T he 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. Selection between gompertz & pearl curves

GENETIC ALGORITHM BASED GREY MODELING: 

GENETIC ALGORITHM BASED GREY MODELING Includes Grey Systems theory Non-linear Grey Bernoulli method Genetic Algorithm

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 ? Grey Systems theory

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 Grey modeling

Grey System based prediction: 

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.

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. Generation of grey sequences

PowerPoint Presentation: 

‘ n’ is the order of the difference equation and ‘m’ is the number of variables. “Grey Model First Order One Variable”. The solution is an exponential curve. T he model fails when there lies a saturation level for the data. GM(N,M ) model GM(1,1) model

Non-linear Grey Bernoulli method: 

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,

Non-linear Grey Bernoulli method: 

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 .

PowerPoint Presentation: 

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

PowerPoint Presentation: 

Step 6 : Take the IAGO on , the corresponding IAGO is defined as where k = 2, 3, . . . , n. This is our predicted value.

PowerPoint Presentation: 

Genetic Algorithm John Holland, University of Michigan (1970’s)

Biological evolution: 

O rganisms 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) Biological evolution

PowerPoint Presentation: 

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

Genetic Algorithm: 

Genetic Algorithms are o ptimization 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 Genetic Algorithm

Stochastic operators: 

Stochastic operators replicates the most successful solutions found in a population at a rate proportional to their relative quality decomposes two distinct solutions and then r andomly mixes their parts to form novel solutions Selection Recombination randomly perturbs a candidate solution Mutation

softwares: 

IBM Inc.’s SPSS Microsoft Excel Palisade Evolver softwares

3) FORECASTING : 

OLED DISPLAY FORECAST OLED LIGHTING FORECAST 3) FORECASTING

FORECASTING OLED DISPLAY TECHNOLOGY: 

PATENT DATA FORECAST RESULT ANALYSIS INFERENCE FORECASTING OLED DISPLAY TECHNOLOGY

PATENT DATA: 

PATENT DATA A ppropriate 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

FORECAST: 

FORECAST PATTERN OBTAINED FROM NON-LINEAR REGRESSION MODEL (LOGISTIC CURVE) FORECASTING

FORECAST: 

FORECAST PATTERN OBTAINED FROM GANGBM MODEL FORECASTING

Widescreen Pictures: 

S-CURVE OF OLED DISPLAY TECHNOLOGY Widescreen Pictures

PHASES OF LIFE: 

PHASES OF LIFE EMEREGNT PHASE : up to 2004 GROWTH PHASE : 2004-2015 MATURITY PHASE : 2015-2020 SATURATION PHASE : 2021-

INFERENCE:: 

INFERENCE: The Technology is currently in its end of growth phase. Will enter its mature stage by 2015. Uncertainty is reduced. High Competition . The mainstream technology in small screen displays . Best time for the Industry players to enter the market. R & D MARKET

OLED LIGHTING TECHNOLOGY: 

PATENT DATA FORECAST RESULT ANALYSIS INFERENCE OLED LIGHTING TECHNOLOGY

PATENT DATA: 

PATENT DATA A ppropriate 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

FORECAST: 

FORECAST PATTERN OBTAINED FROM NON-LINEAR REGRESSION MODEL FORECASTING

FORECAST: 

FORECAST PATTERN OBTAINED FROM GANGBM MODEL FORECASTING

PowerPoint Presentation: 

S-CURVE OF OLED LIGHTING TECHNOLOGY

PHASES OF LIFE : 

PHASES OF LIFE EMEREGNT PHASE : up to 2015 GROWTH PHASE : 2015-2025 MATURITY PHASE : 2025-2034 SATURATION PHASE : 2034-

INFERENCE:: 

INFERENCE: OLED Lighting technology is still in its emergence phase. Huge investments are required. Less Competition. High Opportunities. For Newcomers, this is the best (sometimes the only ) phase to enter the market. R & D MARKET

CONCLUSIONS: 

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. The firms should strive to improve existing technology to cut costs . At the same time, the patent strategy should stress licensing and improvements to technology.

CONCLUSIONS: 

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 invest ing 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.

References : 

References J.P . Martino: A review of selected recent advances in technological forecasting, Technological Forecasting and Social Change 70 (2003); 719–733 . NPTEL course on Management Science Franses , P.H.: A method to select between Gompertz and logistic trend curves, Technological Forecasting and Social Change 46 (1994) ; 45-49 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 D. E. Goldberg , Genetic Algorithm In Search, Optimization and Machine Learning, 1989 E. Kayacan , B. Ulutas , O. Kaynak : Grey system theory-based models in time series pre-diction, Expert Systems with Applications 37 (2010) ; 1784–1789 S.N.Sivanandam , S.N.Deepa : Introduction To Genetic Algorithms, Springer Publishing Company 2007 Genetic Algorithm ware house www.geneticalgorithms.ai-depot.com/Tutorial/Overview obitco.com www.obitko.com/tutorials/genetic-algorithms / www.geneticprogramming.com en.wikipedia.org/wiki/Genetic-algorithm

References : 

References John K. Borchardt : Developments in organic displays; materials today September 2004. HowStuffworks.com http ://electronics.howstuffworks.com/oled.htm novaled.com http ://www.novaled.com/oleds/oled-in-lighting/ 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/ Display Search Report http ://www.displaysearch.com/ Cintelliq Report http ://www.cintelliq.com/ IEEE Spectrum on OLEDs http ://spectrum.ieee.org/tag/OLED Research and Market.com http ://www.researchandmarkets.com/ NanoMarket’s Report on OLEDs http ://nanomarkets.net/ http://www.oled-display.net /

References : 

References 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-revenues-forecast-to-reach-55b-by-2015-says- displaysearch /

Questions & comments: 

Questions & comments