Gain and noise in GaAs/AlGaAs avalanche photodiodes with thin multiplication regions
Bibliographic record
Abstract
Avalanche photodiodes based on GaAs/AlGaAs with separated absorption and multiplication regions (SAM-APDs) will be discussed in terms of capacitance, response to light (gain and noise) and time response. The structures have been fabricated by molecular beam epitaxy introducing a δ p layer doped with carbon to separate the multiplication and the absorption regions. The thickness of the latter layer defines the detection efficiency and the time resolution of the structure, which in turn allows tailoring the device for specific scientific applications. Within the multiplication region a periodic modulation of the bandgap is obtained by growing alternating nanometric layers of AlGaAs and GaAs with increasing Al content; this staircase structure enables the tuning of the bandgap and subsequently provides a well-defined charge multiplication. The use of such staircase hetero-junctions enhances electron multiplication and conversely reduces—at least in principle—the impact of the noise associated to hole multiplication, which should result in a decreased overall noise, when compared to p-i-n diodes composed by a single material. The first part of this paper focuses on the electrical characteristics of the grown structure and on the comparison with the simulated behaviour of such devices. In addition, gain and noise measurements, which have been carried out on these devices by utilizing photons from visible light to hard X-rays, will be discussed and will be compared to the results of a nonlocal history-dependent model specifically developed for staircase APDs.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".