Color Point Stability of LEDs Models for Color Point Stability of LEDs An Introduction to Differential Chromaticity Analysis Presented at Strategies in Light 2017

Models for Color Point Stability of LEDs Strategies in Light 2017 Invited Presentation Models for Color Point Stability of LEDs An Introduction to Differential Chromaticity Analysis Eric Bretschneider EB Designs & Technology

DOE Solid-State Lighting Program Manager Color Point Stability of LEDs A Universal Problem of LEDs and Solid-State Lighting “A light source that shifts in color too much over time is just as useless as one whose lumen output drops below an acceptable threshold*” James Brodrick DOE Solid-State Lighting Program Manager *http://www.ies.org/lda/HotTopics/LED/20.cfm

Importance of Projecting Color Shift Lumen maintenance estimation via LM80/TM21 is the currently accepted metric for projecting LED lifetime Excessive color shift is a failure metric, but . . . A lack of predictive standards or methods means Determining color shift requires excessive amounts of time (test to failure) Du’v’<0.007 in 6,000 hours is not adequate ! ! ! Lumen maintenance estimation via LM80/TM21 is the currently accepted metric for projecting LED lifetime Excessive color shift is a failure metric, but . . . A lack of predictive standards or methods means Determining color shift requires excessive amounts of time (test to failure) Lumen maintenance estimation via LM80/TM21 is the currently accepted metric for projecting LED lifetime Excessive color shift is a failure metric, but . . . A lack of predictive standards or methods means Lumen maintenance estimation via LM80/TM21 is the currently accepted metric for projecting LED lifetime Excessive color shift is a failure metric, but . . .

Importance of Projecting Color Shift L70(6khr) = 21,211 hrs Du’v’(6khr) = 0.0020 LXX(10khr) = 83.3% Du’v’(10khr) = 0.0046 LXX(12khr) = 78.8% Du’v’(12khr) = 0.0068 LXX(8khr) = 87.2% Du’v’(8khr) = 0.0031 Color shift failure before lumen maintenance failure

Importance of Projecting Color Shift L70(6khr) = 842,725 hrs Du’v’(6khr) = 0.0018 LXX(14khr) = 92.51% Du’v’(14khr) = 0.0063 Color shift failure before L90 ! ! !

Today’s Lessons Why does Du’v’ remain constant then start to change? Is it possible to predict Du’v’? Do you need to know details of package construction and materials to attempt a prediction? Reminder: the CIE 1976 u’v’ chromaticity diagram is the most perceptually uniform chromaticity coordinates - (u’, v’) chromaticity coordinates are preferred for quantifying differences in color

LED Color Point Stability Which LED has the best color point stability?

Terminology Relative chromaticity (du’, dv’): Chromaticity relative to chromaticity at time = 0 Incubation: period of time during which chromaticity shift (Du’v’) is essentially constant Recovery: a temporary decrease in chromaticity shift (Du’v’) Emergence: period of time during which chromaticity shift (Du’v’) changes monotonically with time CSn: Time for an LED to exhibit a chromaticity shift (Du’v’) of 0.001 x n CS4  Time to Du’v’ = 0.004 CS7  Time to Du’v’ = 0.007

Du’v’ – a misunderstood metric Du’v’ is a scalar metric Chromaticity change is a vector (2 dimensional metric) Constant Du’v’ does not imply static chromaticity The incubation period is an artifact of the industry standard color shift metric

Understanding Incubation During incubation chromaticity is changing After emergence modeling Du’v’ will give adequate results Predicting emergence requires new models

Differential Chromaticity Analysis Differential Chromaticity (du’*, dv’*) is the rate of change in relative chromaticity over a given time interval dut’* = (du’t+Dt – du’t)/Dt dvt’* = (dv’t+Dt – dv’t)/Dt Differential Chromaticity (du’*, dv’*) is the rate of change in relative chromaticity over a given time interval dut’* = (du’t+Dt – du’t)/Dt dvt’* = (dv’t+Dt – dv’t)/Dt First Principle of Differential Chromaticity: Differential chromaticity is a linear function of time Second Principle of Differential Chromaticity: Keep all data for t ≥ 2,000 hours Differential Chromaticity (du’*, dv’*) is the rate of change in relative chromaticity over a given time interval dut’* = (du’t+Dt – du’t)/Dt dvt’* = (dv’t+Dt – dv’t)/Dt First Principle of Differential Chromaticity: Differential chromaticity is a linear function of time

Analysis Method Calculate relative chromaticities du’t = u’t – u’0 dv’t = v’t – v’0 Calculate differential chromaticities dut’* = (du’t+Dt – du’t)/Dt dvt’* = (dv’t+Dt – dv’t)/Dt Linear fit differential chromaticity data for t ≥ 2,000 hours Project relative chromaticities du’t+Dt = du’t + dut’*Dt = du’t + (aut + bu)Dt dv’t+Dt = dv’t + dvt’*Dt = dv’t + (avt + bv)Dt

Example 1 6,000 hour data shows barest hint of emergence Incubation 6,000 hour data shows barest hint of emergence DCA input data shows almost no variance Almost immediate emergence predicted Reasonable agreement to limit of data set 6,000 hour data shows barest hint of emergence DCA input data shows almost no variance Almost immediate emergence predicted 6,000 hour data shows barest hint of emergence 6,000 hour data shows barest hint of emergence DCA input data shows almost no variance

Example 1 DCA input = 2 – 14 khrs Close agreement over long time intervals Rolling time window not required for accuracy

Example 1 Behavior Rapid initial shift towards yellow/orange Long term shift towards blue Blue shift begins ~3khrs Blue shift firmly established ~5khrs

Example 1 Model developed projecting forward from 2,000 hour data point Projecting from final data point increases accuracy Exceptional accuracy when “close” to CS7

Example 2 6,000 hour data shows apparent stability DCA predicts (surprising) recovery and then emergence 14khr data mirrors the prediction 8khr DCA prediction closely matches full data 6,000 hour data shows apparent stability DCA predicts (surprising) recovery and then emergence 14khr data mirrors the prediction 6,000 hour data shows apparent stability DCA predicts (surprising) recovery and then emergence 6,000 hour data shows apparent stability

Example 2 Longer term predictions support emergence at 15 - 16khr Slight changes in CS7 using data out to 8 - 14khr

Example 2 Behavior Initial shift towards blue Long term shift towards yellow Yellow shift does not start until ~5khr Yellow trend firmly established 8 - 9khr

Example 2 Reminder: shift change at 5,000 hours Using 1,000 hour data sometimes leads to optimistic results 6khr  CS7 error ~15% 7khr  CS7 error ~9% 8khr  CS7 error ~3%

Example 3 6,000 hour data shows apparent stability DCA predicts recovery followed by emergence Full data shows recovery DCA matches full data Extrapolating forward still predicts emergence 6,000 hour data shows apparent stability DCA predicts recovery followed by emergence Full data shows recovery DCA matches full data 6,000 hour data shows apparent stability DCA predicts recovery followed by emergence 6,000 hour data shows apparent stability DCA predicts recovery followed by emergence Full data shows recovery 6,000 hour data shows apparent stability

Example 3 Behavior Initial shift towards yellow/green Long term shift towards blue begins >7khr DCA predicted reverse shift before it occurred

Example 3 Reminder: final blue shift begins after 7khr data Using 1,000 hour data leads to optimistic results Reasonable convergence rate

Example 4 6,000 hour data looks exceptional Du’v’ ≤ 0.0006 2 - 6khr change in Du’v’ = 0.000212 DCA predicts imminent emergence 6khr DCA pessimistic 7khr DCA accurate 6,000 hour data looks exceptional Du’v’ ≤ 0.0006 2-6khr change in Du’v’ = 0.000212 DCA predicts imminent emergence 6khr DCA pessimistic 6,000 hour data looks exceptional Du’v’ ≤ 0.0006 2-6khr change in Du’v’ = 0.000212 6,000 hour data looks exceptional Du’v’ ≤ 0.0006 2-6khr change in Du’v’ = 0.000212 DCA predicts imminent emergence 6,000 hour data looks exceptional Du’v’ ≤ 0.0006

Example 4 Behavior Initial shift towards blue Second shift towards yellow Third shift towards blue Final blue shift begins >5khr

Example 4 Reminder: double (180°) change in direction of chromaticity shift Rapid convergence achieved despite complex chromaticity behavior

Behavior of Examples 1-4 Example data sets exhibited variety of sometimes complex behaviors Multiple direction changes Late (>7khr direction change) Despite radically different chromaticity shifts DCA is able to capture and model behavior with no adjustable parameters

LED Performance can now be Predicted (from LM-80 data) Lifetime Color Shift of LEDs Examples 1- 4 Predictions of these 4 Color Shifting LEDs (using DCA algorithms with 8Khr of LM-80 data)

Comments on the DCA model Powerful predictive capabilities Extremely efficient noise filter – even poor correlations for differential chromaticity parameters yield reasonably accurate estimates Able to accurately model large time spans of complicated data Inherently unstable model – it will always give finite values for CS7; estimates converge as more data is included in the analysis Package format and material agnostic

Thank you Questions/Comments . . . Eric Bretschneider eric@led.expert eric.b@quarkstar.com

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