OLED Optimization Strategies Using Definitive Screening Designs

OLEDWorks LLC©
Strategies for Optimization of an
Organic Light Emitting Diode
David Lee
Director, Quality and Reliability
OLEDWorks LLC
dlee@oledworks.com
https://www.youtube.com/watch?v=OHwYpev2-4Q

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Quiz: How did I fracture my arm in 3 places?

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Agenda
What’s an OLED?
OLED Optimization:
Definitive Screening Designs & Models
Reliability
Accelerated Degradation
Time to Failure Modeling
Multivariate Analyses
Principal Components
Partial Least Squares

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Facilities in Rochester, NY and Aachen, Germany
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What is OLEDWorks?

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What do people love about OLED lighting?

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Light Sources and Luminaires

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OLEDWorks – How we participate
Collaboration
Collaboration
Collaboration

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OLEDWorks – What We Do
WE MAKE OLED LIGHT ENGINES
OLED material, formulation, process and quality/reliability experts
OLED lighting manufacturing innovation
Aachen: Make the world’s brightest panels, high volume capacity
Rochester: Disruptive low cost structure, amber, low volume, scalable
OLED collaboration and integration
Driver and electronics support, technical support, supplier collaboration
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What’s an OLED?
OLEDs are electroluminescent electrical devices that can be tuned to emit a range
of visible light in response to an applied voltage/current
•There can be
anywhere up to ~20
layers in an OLED
device
•Each layer can
consist of a single
component or
mixtures of multiple
components
•The total thickness of
the organic layers is
on the order of 200-
300 nm (0.2-0.3 )

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Basic OLED Structure
5 – 10 V

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Design of High Efficiency OLEDs

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How Big is a Micron?
•Individual layers and deposition rates are measured in Nanometers (10-9 m) and
Angstroms/sec (10-10 m)

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Definitive Screening Designs
2011 - Introduced by Jones and Nachtsheim
2013 – Jones and Nachtsheim show 2-level categorical factors
can be incorporated
2015 – Jones introduces concept of ‘Fake Factors’ at JMP
Discovery Summit to improve power and signal detection
2016 - Jones, B. and Nachtsheim, C.J. (2016), “Effective Model
Selection for Definitive Screening Designs,” Technometrics,
forthcoming
2016 – Two-Stage Effective Model Selection implemented in
JMP version 13

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What is a Definitive Screening Design?
Inherently small designs with the minimum number of runs -> n=2m+1
If there are m=6 factors then the minimum DSD can be run in just 13 trials!
DSDs are three-level designs that are valuable for identifying main effects and second-order
interactions in a single experiment.
A minimum run-size DSD is capable of correctly identifying active terms with high probability if the
number of active effects is less than about half the number of runs and if the effects sizes exceed
twice the standard deviation
DSDs utilize a methodology called Effective Model Selection takes advantage of the unique structure
of definitive screening designs.
The Effective Model Selection for DSDs algorithm leverages the structure of DSDs and assumes strong effect
heredity to identify active second-order effects.
DSD’s can be augmented with properly selected extra runs to significantly increase the design’s ability
to detect second-order interactions.
These extra runs correspond to fictitious factors that are included in the design but not in the analysis
Jones and Nachtsheim* (2016) report power for detecting 2FI and quadratic effects is greatly
improved especially when there are fewer active Main Effect
*Jones, B. and Nachtsheim, C.J. (2016), “Effective Model Selection for Definitive Screening Designs,”
Technometrics, forthcoming

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Introduction of extra runs via including fictitious
factors in the design of the experiment
In a DSD Main Effects are orthogonal to 2FI, two-stage
modeling splits the response into two new
components (i.e., responses)
Modeling occurs in two steps:
Y Main Effects
Y Second Order Effects
Assumes model heredity
What is Effective Model Selection?
Referred to as Two-Stage in v13

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Simulation Comparisons Two-Stage vs. Stepwise
Comparison for DSD with
6 factors and 17 runs (i.e.
2 fake factors)
Power for detecting 2FIs
and Quadratic effects is
much higher for the new
method especially when
fewer MEs are active
Bradley Jones, “Analysis of Definitive Screening Designs” JMP Discovery Summit 2015

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DSD Example: OLED Device
Experiment consisted of 6
Factors (A thru F)
conducted in 2 Blocks
This talk will discuss 6
responses
‘Typical’ 6-factor DSD
would have 2m+1=13 runs
This design incorporates 2
additional factors in the
set-up resulting in 4
additional runs and 1 run
for the Block effect totaling
18 runs.

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Correlation & Power Comparison
JMP12: n=14 runs JMP 13: 2 Addt’l Factors, n=18 runs
• DSD is for 6 Factors run in 2 Blocks
• Power for detecting 2FIs and Quadratic effects is much higher for the two-
stage method especially when fewer MEs are active
Jones, B. and Nachtsheim, C.J. (2016), “Effective Model Selection for Definitive
Screening Designs,” Technometrics, forthcoming

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Modeling of Y Main Effects
Each foldover pair sums to zero and the centerpoint estimates are zero

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Modeling of Main Effects Continued
Since the foldover pairs sum to zero there are 8
independent values ( df = 8 )
There are 6 Factors; therefore, there are 8-6=2 df
for estimating variability
According to Jones*, an estimate of σ2 can be
obtained by summing the squared residuals and
dividing by the remaining degrees of freedom
Use this estimate to perform t tests on each
coefficient and determine significance
*Jones, B. (2015),JMP Discovery Summit

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Identifying Significant 2nd Order Effects
Modeling of the 2nd order interaction in a DSD
assumes model heredity, so only interactions
and quadratic effects from significant main
effects are considered
JMP performs all subsets regression stopping
when the MSE of the ‘best’ model is not
significantly larger than the estimate of σ2

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How it’s Done in JMP v13

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DSD – Fit Least Squares
• Can’t save prediction
formula in the DSD Fit
platform
• Limited options for
Maximizing
Desirability
• Need to make and
run model, then you
can follow thru with
all of the options for
evaluating the model

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Response Y2
The predicted model
didn’t seem to correlate
with the expected
physics of an OLED
device
Device experts and
optical models didn’t
seem to agree with the
DSD model
Alternate methods were
explored

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Response Y2 – Alternate Modeling Strategies
Forward Stepwise w/ AICc Gen Reg w/ Elastic Net and AICc

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Model Comparison
Stepwise seems to
outperform models
generated by the DSD as
well as Gen Reg
The difference seems to
boils down to how one
data point is handled
Inspection and
additional testing
showed no obvious issue
with this device
We had no reason to
believe there was an
issue with this point as it
didn’t exert extensive
leverage

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Power Analysis for ‘Very Active’ Model

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Prediction Profiler Results

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Simulator & Defect Model
Factor variability was
estimated from calibration
data
Two responses account for
nearly all predicted losses
Upstream process is
causing significant
variability to Y1
Y7 seems to be predicting
unusually higher than
expected/observed

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Adding Additional Random Noise
In JMP an option exists to add additional noise to a predicted response
This study is focusing on the organic stack and does not include variability from either upstream
or downstream processes
There was an issue in a separate process that was inducing variability into Y1
The effect of the additional variability can be incorporated into the simulations and subsequent
defect profiler
Changes to Predicted Y1
Without
additional
noise
With
additional
noise

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Next Steps & Defect Profiler
Y6 is rarely used to make significant decisions. Removed from optimization studies.
Involve Marketing & Business Development regarding problematic responses
Y4 was generally shifted. Reformulated to bring within spec window.
Address upstream problem that is significantly affecting Y1
Eliminating external effect would significantly reduce Y1’s contribution to the overall defect
model
Illustrated to Management, Engineering and Marketing the trade-offs via performing
‘What-If’ Scenarios

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DSD Conclusions
New in v13! The concept of introducing extra runs
via fictitious factors and the Effective Model
selection or Two-Stage method for constructing
and analyzing designs was demonstrated through
the optimization of an OLED device
Alternative methods such as Stepwise and
Generalized Regression were compared
Simulation studies were used to identify problem
areas and understand projected defect rates

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DEGRADATION DATA AND
RELIABILITY ANALYSES
Region 1 (Infant Mortality/Early Life)
•Often related to manufacturing or quality
and to processing/assembly issues
•Stress screening or Burn in tests may be
very effective
Region 3 (Wearout)
•Failures are due to wearout mechanisms
•Delay of onset possible through design
•Region begins for electronic components after 40 yr.
•Mechanical parts often reach wearout during
operating life
Region 2 (Random/Constant FR)
•Failures related to minor processing/assembly variations
•Most products are at acceptable failure rate level
•Reduction in operating stress and/or increase in design
robustness can reduce FR
Burn-in Period Useful Life Wearout
Still using devices from 18-run DSD

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What’s the Big Deal About Reliability?
Given the above statement, no one has unlimited resources so trade-offs need to be
considered. Manufacturers in today's marketplace are faced with the difficult or
seemingly impossible task of obtaining accurate failure data for their products in an
cost-effective and timely manner.
This occurs for several reasons including very long product lifetimes, very high or
100% duty cycles or the churn between product offerings is too rapid.
Accelerated and other test methods are necessary because manufacturers may not
be able to test new designs and products under normal operating conditions.

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OLED Accelerated Fade Degradation Test Methods
Like many solid state devices and
electronic components, early life
failures are present requiring Burn-
In testing
OLEDs tend to gradually fade over
time following a function similar to
an exponential decay
There are industry standards for
determining end of useful life but
the time for a device to reach 70%
of it’s initial radiance is a fairly
common metric – T(70)
OLED lifetimes are approaching 50K
hours (>5 years) requiring the need
for reliable accelerated tests

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Degradation vs. Time to Failure Analysis
http://www3.stat.sinica.edu.tw/statistica/oldpdf/A6n32.pdf

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Time to Failure Analysis
This small simulated data
set looks like it could be
from a Weibull distribution
with a shape parameter of
1.68 indicating a wear-out
mechanism associated with
the later stages of a bath
tub curve
What if we only knew times to failure?

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Detection of Anomalies:
This is the same data shown on the previous slide
I’d feel comfortable saying there
was probably something going on
with these samples
What about this device? Is it
different or just an early failure
from the null distribution?

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Using Degradation Data for Life Data Analyses
Fit the degradation data using an established model such as exponential decay,
linear, etc.
Extrapolate to the failure point (i.e., T(80) or T(70))
Determine an appropriate Life Stress model (Arrhenius, Inverse Power, etc.)
Determine the pdf at each stress level and compare fit statistics (i.e. Beta)
If assumptions still hold, apply Life Stress model and make predictions
So, how do we take data that
hasn’t failed and predict times
to failure?
The answer is to extrapolate
curve and estimate the failure
time.

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Analyze>Reliability and Survival>Degradation
Fade<<New
Column("Stretched
Exponential", Numeric,
Continuous,
Formula(Parameter( {a=1,
b=-0.01, c=0.2},
a*exp(b*:Hours^c))));

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Analyze>Specialized Modeling>Nonlinear
Orders of magnitude faster than the Degradation platform
Fade<<New Column("Stretched Exponential", Numeric, Continuous,
Formula(Parameter( {a=1, b=-0.01, c=0.2},
a*exp(b*:Hours^c))));
f=Fade<<Nonlinear(
Y( :Name("Std Light Output1") ),
X( :Name( "Stretched Exponential" ) ),
Iteration Limit( 100000 ),
Unthreaded( 1 ),
Newton,
Finish,
By( :Dev ID ),
Custom Inverse Prediction( Response( 0.7 ),
Term Value( Hours( . ) ) )
);
f_rep = f <<report;
rep=Report( f[1] )[Outline Box (3)][Table Box(1)] << Make
Combined Data Table;
rep=current data table()<<Set Name("MSE Report");
rep=current data table();
rep<<Save(::results || " MSE Report.jmp");
life=Report( f[1] )[Outline Box (6)][Table Box(1)] << Make
Combined Data Table;
life<<Current Data Table<<Set Name("Combined T70
Predictions");
life=Current Data Table();
life<<New Column("Exp No", character, formula(Substr( :Dev ID
, 1, 10 )));
life<<Save(::results || " Combined T70 Predictions.jmp");

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Digression: Inverse Power Model to Determine Acceleration Factor
Fit Life-By-X Platform
This is an extremely
powerful platform for
reliability
professionals
Introduced in JMP v8?
This is where the user
decides on the
appropriate Life Stress
model and pdf

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Simulated Inverse Power Model
Simulated data with 3 stress levels (10, 20 &
30) with a Weibull distribution
Assuming a use condition or baseline of 5

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Parameter Estimates and Acceleration Factor Profiler
This model assumes there is a
constant shape parameter, β,
across the stress levels
We use the Acceleration Factor to
adjust predicted accelerated
failure times to use conditions
There can be plenty of pitfalls with Accelerated Testing!!

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Back to the OLED Life Data: Modeling Choices
Unfortunately, we ‘lost’ one device.
Glass devices and cement floors don’t mix! Gravity keeps coming back to haunt me!
Definitive Screening Design
Get error message indicating that one foldover run was missing but did complete the analysis
Data is not Normally distributed
Hope the Central Limit Theorem helps out
Parametric Regression
Designed to accommodate non-Normally distributed data
Not able to handle supersaturated designs
Pretty much limited to modeling main effects
Stepwise Regression with an AICc Stopping Criteria
Able to handle supersaturated designs but not Weibull distributed data
Generalized Regression
New in JMP v13, Gen Reg can incorporate Weibull distributions!
Quantile Regression
Not appropriate for this data set and test objective
More appropriate for larger sample sizes.
More common use is for hierarchical or multi-level modeling
Might be applicable if I thought there was a defective sub-population but that should become evident
through other methods in the Life Distribution Platform

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Generalized Regression
In addition to the standard prediction profiler, there are specific
profilers for estimating failure probabilities, quantiles, etc. Some are
shown above.
The model seems a little ‘weak’. Experience tells us that other
factors should be active.
Recall, we only have 17 devices on test.

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Reliability Conclusions
New in v13! Generalized Regression now supports
a Weibull distribution and provides profilers
consistent with other reliability platforms
Opens new possibilities for model determination
OLED experience tends to make us think there
should be more active factors
Follow-up studies underway
Also new in v13, but not discussed today, is the
Cumulative Damage platform for step-stress
testing
OW has successfully been evaluating this new feature

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Principal Components Analysis
• PC performed on covariances as the units are the same
• 2 components account for just over 95% of the total variance
• Analyze>Multivariate Methods>Principal Components can not accommodate Nominal factors
(i.e., Block)
• JMP v13 can fit polynomial, interaction, and categorical effects, using the Partial Least Squares
personality in Fit Model

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Multivariate Conclusions
Demonstrated Two-Stage DSD Modeling of Principal Components
Could be somewhat misleading because the initial PCA was performed
without the Block Factor
The results for PC1 were very similar to some of the initial DSD models
JMP v13 can fit polynomial, interaction, and categorical effects, using
the Partial Least Squares personality in Fit Model
This incorporated the effect due to Blocks
Ultimately, a 4 Factor model was selected
Each factor revealed unique insights that might have been overlooked
For example, the 4th factor might be viewed as noise but really it
explains subtle contributions that affect color point

OLED Optimization Strategies Using Definitive Screening Designs

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