Characterization of low temperature InGaAs-InAlAs semiconductor photo mixers at 1.55 <i>μ</i>m wavelength illumination for terahertz generation and detection
Bibliographic record
Abstract
The structural, optical, and electrical properties of undoped and Be doped lattice matched InGaAs–InAlAs multiple quantum well structures, grown by molecular beam epitaxy (MBE) at low (∼250 °C) and normal (∼450 °C) growth temperatures, have been investigated in detail. Double crystal x-ray diffraction studies showed that the thickness of the low temperature (LT) grown quantum well (QW) layers decrease with post growth annealing, while the normal temperature grown QW layers retain their initial thickness. This behaviour is associated with the As precipitation and is the first evidence and report of a direct observation of this phenomenon in LT InGaAs–InAlAs QWs. Room temperature photoluminescence (PL) measurements revealed signs of optical activities in the LT undoped and lower doped structures suggesting that the native defects in LT InGaAs–InAlAs are not sufficient to completely inhibit band to band recombination. Optimal combination of doping, including a modulation doped structure, and post growth annealing temperature results in materials with sub-picoseconds lifetimes (&lt;200 fs) and a resistivity of ∼107 Ω/sq, which is a high value for this material. The results imply the possibility of fabricating efficient photo-mixers operating at the telecom wavelength of 1.55 μm for THz imaging or other optoelectronic applications.
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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 | 0.000 |
| 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 | 0.000 | 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".