future of oleds as lighting solutions & displays
DESCRIPTION
STUDY ON OLED LIGHTS & DISPLAYS, FORECAST ON FUTURE OF OLEDs METHODOLOGY: GENETIC ALGORITHM based GREY MODELINGTRANSCRIPT
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.
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