OLED technology has potential applications in lighting and displays. It provides advantages over traditional lighting solutions like LEDs and fluorescent bulbs by being thinner, lighter, more flexible, and more energy efficient. The document analyzes OLED technology, including its basic working principles and structure. It also reviews the evolution of different lighting technologies and current major players in OLED development. Finally, it outlines methods for forecasting the future growth of OLEDs using technology growth curve models and a genetic algorithm based grey modeling approach.

1. FUTURE OF OLED
AS
LIGHTING SOLUTION
12th Nov, 2012
SHAMEER P.H.
m.Tech - technology management

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

5. TECHNOLOGY MANAGEMENT


TECHNOLOGY
MANAGEMENT
&
TECHNOLOGY
FORECASTING

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

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. 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. 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. TECHNOLOGY FORECASTING

HOW?

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. FUTURE OF OLEDS AS
LIGHTING SLOUTION
1.
2.
3.
4.
OLED TECHNOLOGY- A
REVIEW
METHODOLOGY
FORECASTING RESULTS
AND DISCUSSIONS
CONCLUSION

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

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

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. TYPES &EVOLUTION OF LAMPS

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. EVOLUTION OF LAMPS

22. IN TERMS OF LUMINOUS EFFICACY

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

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

33. APPLICATIONS

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

36. OLED DISPLAYS

Thinner, lighter and flexible.

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. OLED DISPLAYS

LARGE FIELD OF VIEW.

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

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. applications



Mood Lighting
Object Illumination
General Illumination

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. OLED AND OTHERS ….

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

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

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. 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. 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. forecasting using
growth curve MODELS

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

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. 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. GENETIC ALGORITHM BASED
GREY MODELING
Includes
a) Grey Systems theory
b) Non-linear Grey Bernoulli method
c) Genetic Algorithm

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

69. Genetic Algorithm
John Holland, University of Michigan (1970‟s)

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. 
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. 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. 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. softwares

IBM Inc.‟s SPSS

Microsoft Excel

Palisade Evolver

75. 3) FORECASTING
A.
B.
OLED DISPLAY
FORECAST
OLED LIGHTING
FORECAST

76. FORECASTING
OLED DISPLAY TECHNOLOGY




PATENT DATA
FORECAST
RESULT ANALYSIS
INFERENCE

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. 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. FORECAST
PATTERN
OBTAINED
FROM
GANGBM
MODEL
FORECASTI
NG

80. WIDESCREEN
PICTURES
S-CURVE OF OLED DISPLAY TECHNOLOGY

81. PHASES OF LIFE
EMEREGNT PHASE
up to 2004
:
GROWTH PHASE
2004-2015
:
MATURITY PHASE
2015-2020
:

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. OLED LIGHTING TECHNOLOGY




PATENT DATA
FORECAST
RESULT ANALYSIS
INFERENCE

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. 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. FORECAST
PATTERN
OBTAINED
FROM
GANGBM
MODEL
FORECASTI
NG

87. S-CURVE OF OLED LIGHTING TECHNOLOGY

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

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. 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. 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. 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. 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. 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. QUESTIONS
&
COMMENTS