Thermal Droop Effects in AlGaN Ultraviolet-C Light-Emitting Diodes
Bibliographic record
Abstract
Al-rich AlGaN based light-emitting diodes (LEDs) operating in the ultraviolet-C (UV-C) spectral wavelength (<280 nm) are important for various critical applications. However, to date, the efficiency of UV-C LEDs remains significantly low in these challenging wavelengths. Here, we have demonstrated that, unlike several established reasons, thermal effects (including self-heating and thermal droop) could cause severe efficiency degradation in UV-C LEDs. Infrared thermal imaging was utilized to measure the accurate internal temperature of epitaxially grown and fabricated UV-C LED heterostructure on nanopatterned sapphire substrate (NPSS). The temperature-dependent measurements show reduced light output and efficiency when operating at increasing ambient temperatures up to 85 °C. We have compared UV-C LED chips of different sizes to correlate these findings with the device area. For a 250 × 500 μm 2 device, the peak light output power (LOP) was ∼29.4 mW at 340.5 A/cm 2, while the peak LOP for a 500 × 500 μm 2 device was ∼45 mW at 270.3 A/cm 2 . Though the maximum achievable efficiency was significantly limited by thermal droop, the external quantum efficiency in the smaller device was measured to be 0.8% higher because of improved light extraction efficiency. Thermal imaging-based experiments and heat diffusion equation-based numerical analysis indicate that smaller devices can sustain higher temperatures until they reach thermal droop.
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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".