FUTURE OF OLED AS

LIGHTING SOLUTION

SHAMEER P.H. m.Tech - technology management12th Nov, 2012

ABSTRACTIn 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

Objective: Forecast the potential of

OLEDs in the field of Lighting and Displays using Growth Curve models and GA based Grey Bernoulli Model.

“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

FORECASTING

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)

TECHNOLOGY MANAGEMENT Aims:

using technology as a source of competitive advantage.

Deals with: Developing technology strategies Developing/Acquiring

technologies Using Technologies.

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

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 FORECASTINGA 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 HOW?

Technology lifecycle

The technology’s performance improvement follows the S-Curve with1) Embryonic Phase2) Growth Phase3) Maturity Phase4) Saturation Phase5) Declining Phase

1. OLED TECHNOLOGY- A REVIEW

2. METHODOLOGY3. FORECASTING RESULTS

AND DISCUSSIONS4. CONCLUSION

FUTURE OF OLEDS AS LIGHTING SLOUTION

This Section deals with Basics of Luminescence Evolution and Types of

Light Bulbs OLED technology

1)OLED TECHNOLOGY- A REVIEW

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

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

COOL LIGHT Caused by movement of electrons from more

energetic state to less energetic state. Chemiluminescence, Electroluminescence,

Bioluminescence…. Fluorescence &Phosphorescence

FLUORESCENCE

PHOSPHORESCENCE

The 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 OF LAMPS INCANDESCENT LAMPS

HALOGEN LAMPS

FLUORESCENT LAMPS

COMPACT FLLORESCENT LAMPS

HIGH INTENSITY DISCHARGE LAMPS

LOW PRESSURE SODIUM LAMPS

SOLID STATE LIGHTING

PRE ELECTRICAL• OIL• CAN

DLE

INCANDESCENT• SWAN

& EDISON 1870

HIGH INTENSITY DISCHARGE• MERCU

RY (1904)

• METAL HALIDE (1961)

• HP SODIUM (1965)

FLUORESCENT• TUBE

(1938)• CFL

(1981)

SSL• LED

(2000)• OLED

EVOLUTION OF LAMPS

Click icon to add picture

IN TERMS OF LUMINOUS EFFICACY

DEALS WITHwhat is an OLED?structure of OLED,working principle of OLEDits applications and advantages.

OLED TECHNOLOGY

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 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.. 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.. 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.

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 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 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..

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

SMALL DISPLAYS

LARGE DISPLAYS

PMOLED AMOLED WHITE OLED

LIGHTING SOLUTIONS

APPLICATIONS

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

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

OLED DISPLAYS Thinner, lighter and flexible.

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.

OLED DISPLAYS

LARGE FIELD OF VIEW.

OLED DISPLAYS FAST RESPONSE TIME

LCD (200ms)

OLED (10µs)

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

Mood Lighting Object Illumination General Illumination

applications

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

OLED AND OTHERS ….

WHO ALL ARE IN THE FIELD..

Source: HENDY Consulting

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… 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

1. DATA COLLECTION

2. FORECASTING BY NON-LINEAR REGRESSION.

3. FORECASTING BY GA BASED N GREY BERNOULLI METHOD.

4. RESULTS AND DISCUSSIONS

5. INFERENCE.

2)METHODOLOGY

DATA COLLECTIONPatent Data from LexisNexis Database is used to forecast.

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

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.

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

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

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

1) the Pearl curve2) the Gompertz curve

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

Finding the coefficients

Gompertz Model

Logistic Model

Linear transformation of these equations using natural logarithm will lead to:

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

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.

Selection between

gompertz & pearl curves

GENETIC ALGORITHM BASED GREY MODELING

Includesa)Grey Systems theoryb)Non-linear Grey Bernoulli

methodc)Genetic Algorithm

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 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 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.

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

‘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.

The model fails when there lies a saturation level for the data.

GM(N,M) model GM(1,1) model

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

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.

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

Step 6 : Take the IAGO on , the corresponding IAGO is defined as

where k = 2, 3, . . . , n. This is our predicted value.

Genetic Algorithm

John Holland, University of Michigan (1970’s)

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)

Biological evolution

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 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

Genetic Algorithm

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 randomly mixes their parts to form novel solutions

Selection Recombination

randomly perturbs a candidate solution

Mutation

IBM Inc.’s SPSS

Microsoft Excel

Palisade Evolver

softwares

A. OLED DISPLAY FORECAST

B. OLED LIGHTING FORECAST

3) FORECASTING

PATENT DATA FORECAST RESULT ANALYSIS INFERENCE

FORECASTINGOLED DISPLAY TECHNOLOGY

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

FORECAST

PATTERN OBTAINED

FROM NON-LINEAR

REGRESSION MODEL

(LOGISTIC CURVE)

FORECASTING 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

20.4

27.4

90

20.4

30.6

38.634.6

31.6

45.9 46.9 45 43.9

East West North

FORECAST

PATTERN OBTAINED

FROM GANGBM MODEL

FORECASTING

S-CURVE OF OLED DISPLAY TECHNOLOGY

WIDESCREEN PICTURES

PHASES OF LIFE

EMEREGNT PHASE : up to 2004

GROWTH PHASE : 2004-2015

MATURITY PHASE : 2015-2020

SATURATION PHASE : 2021-

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

PATENT DATA FORECAST RESULT ANALYSIS INFERENCE

OLED LIGHTING TECHNOLOGY

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

FORECAST

PATTERN OBTAINED

FROM NON-LINEAR

REGRESSION MODEL

FORECASTING

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

20.4

27.4

90

20.4

30.6

38.634.6

31.6

45.9 46.9 45 43.9

East West North

FORECAST

PATTERN OBTAINED

FROM GANGBM MODEL

FORECASTING

S-CURVE OF OLED LIGHTING TECHNOLOGY

PHASES OF LIFE

EMEREGNT PHASE : up to 2015

GROWTH PHASE : 2015-2025

MATURITY PHASE : 2025-2034

SATURATION PHASE : 2034-

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

CONCLUSIONSThe 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

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.

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. obitco.com www.obitko.com/tutorials/genetic-algorithms/

10. www.geneticprogramming.com

11. en.wikipedia.org/wiki/Genetic-algorithm

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. Zill, D. G. and Cullen, M. R.:Advanced Engineering Mathematics 2nd ed. Massachusetts: Jones and Bartlett, 2000

16. 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

17. 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/

References 24. http://ledsmagazine.com/25. 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.

26. About.com http://inventors.about.com/od/lstartinventions/a/lighting.htm27. megavolt.co.il http://www.megavolt.co.il/Tipsandinfo/types ofbulbs.html28. 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.

29. 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.

30. OLED Display.net Report on OLED Revenue http://www.oled-display.net/oled-revenues-forecast-to-reach-55b-by-2015-says- displaysearch/

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