Near-field photometry for organic light-emitting diodes
Bibliographic record
Abstract
Organic Light Emitting Diode (OLED) technology is rapidly maturing to be ready for next generation of light source for general lighting. The current standard test methods for solid state lighting have evolved for semiconductor sources, with point-like emission characteristics. However, OLED devices are extended surface emitters, where spatial uniformity and angular variation of brightness and colour are important. This necessitates advanced test methods to obtain meaningful data for fundamental understanding, lighting product development and deployment. In this work, a near field imaging goniophotometer was used to characterize lighting-class white OLED devices, where luminance and colour information of the pixels on the light sources were measured at a near field distance for various angles. Analysis was performed to obtain angle dependent luminous intensity, CIE chromaticity coordinates and correlated colour temperature (CCT) in the far field. Furthermore, a complete ray set with chromaticity information was generated, so that illuminance at any distance and angle from the light source can be determined. The generated ray set is needed for optical modeling and design of OLED luminaires. Our results show that luminance non-uniformity could potentially affect the luminaire aesthetics and CCT can vary with angle by more than 2000K. This leads to the same source being perceived as warm or cool depending on the viewing angle. As OLEDs are becoming commercially available, this could be a major challenge for lighting designers. Near field measurement can provide detailed specifications and quantitative comparison between OLED products for performance improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".