Dual band HEIWIP detectors with nitride materials
Bibliographic record
Abstract
Detection of both UV and IR radiation is useful for numerous applications such as firefighting and military sensing. At present, UV and IR dual wavelength band detection requires separate detector elements. Here results are presented for a GaN/AlGaN single detector element capable of measuring both UV and IR response. The initial detector used to prove the dualband concept consists of an undoped AlGaN barrier layer between two highly doped GaN emitter/contact layers. The UV response is due to interband absorption in the AlGaN barrier region producing electron-hole pairs which are then swept out of the barrier by an applied electric field and collected at the contacts. The IR response is due to free carrier absorption in the emitters and internal photoemission over the work function at the emitter barrier interface, followed by collection at the opposite contact. The UV threshold for the initial detector was 360 nm while the IR response was in the 8-14 micron range. Optimization of the detector to improve response in both spectral ranges will be discussed. Designs capable of distinguishing the simultaneously measured UV and IR by using three contacts and separate IR and UV active regions will be presented. The same approach can be used with other material combinations to cover additional wavelength ranges, e.g. GaAs/AlGaAs NIR-FIR dual band detectors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".