Efficient light emitting diodes by photon recycling and their application in pixelless infrared imaging devices
Bibliographic record
Abstract
The success of the pixelless imaging concept using a quantum well infrared photodetector integrated with a light emitting diode (QWIP–LED) depends critically on the extent of spatial lateral spreading of both photocurrent generated in the QWIP and near infrared (NIR) photons emitted by the LED as they escape from the device layers. According to the photon recycling model proposed by Schnitzer et al. [Appl. Phys. Lett. 62, 131 (1993)] there appears to be a trade-off between a high LED external quantum efficiency and a small photon lateral spread, the former being a necessary condition for achieving high detector sensitivity. This lateral spreading due to multireflections and reincarnations of the NIR photons could potentially degrade the image quality or resolution of the device. By adapting Schnitzer’s model to the QWIP–LED structure, we have identified device parameters that could potentially influence the NIR photon lateral spread and the LED external efficiency. In addition, we have developed a simple sequential model to estimate the crosstalk between the incoming far infrared image and the up-converted NIR image. We have found that the thickness of the LED is an important parameter that needs to be optimized in order to maximize the external efficiency and to minimize the crosstalk. A 6000-Å-thick LED active layer should give a resolution of ∼30 μm and an external efficiency of ∼10%.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".