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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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OLEDWorks enables you to revel in possibility.

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Be Unlimited with Light.

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Over 400 years of OLED expertise!

Facilities in Rochester, NY and Aachen, Germany

Supporting your OLED experience

What is OLEDWorks?

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OLED Displays

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OLED Lighting

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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

� Department of Energy test site for new OLED technologies!!

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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 µ)

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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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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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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 ModelFactor 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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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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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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Determine Appropriate Model Terms

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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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Parametric Survival Regression

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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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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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MULTIVARIATE ANALYSIS

Still using devices from 18-run DSD

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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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DSD Modeling of PC1

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Partial Least Squares Model

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NIPALS Fit with 4 Factors

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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

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Thank You & Questions

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Design Freely

Organic Light Emitting Diodes

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APPENDIX & MISCELLANEOUS

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