Influence of the injection current on the degradation of white high-brightness light emitting diodes
Bibliographic record
Abstract
Since high-power LEDs show great potential in reducing energy consumption worldwide, a great deal of research has been performed to understand their degradation rate. As reported in many publications, temperature is of critical importance so lifetests are mainly based on the internal temperature of the junction (Tj). A common testing method is to overdrive the LED with high current in order to cause self-heating. However, by doing so, it is assumed that current does not produce self-degradation. This topic is of great importance nowadays because of the recent development of LEDs used to increase operating current. We have conducted a lifetest on LEDs to isolate the influence of current by using a thermally-controlled heatsink to keep the same Tj for different driving currents. This paper presents the experimental setup with the associated protocol used in the experiment. We also present preliminary results obtained from two high-power white LEDs. These were stressed at currents ranging from 350 mA to 1000 mA and at temperatures ranging from 75°C to 150°C. To our knowledge, this type of measurement has not been reported in the literature. In the future, we would like to use a Weibull statistical model to study the combined effects of temperature and current on the degradation of LEDs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".