Measurements and calculations of spontaneous emission of tensile and compressively strained multiple quantum-wells structures
Bibliographic record
Abstract
The characterization of material and structural properties is essential in the development of high-performance optoelectronics devices. The gain and spontaneous emission of semiconductor emitters are intrinsically related, and knowing one determines the other. In this paper, we report on a comparison between the measured and calculated spontaneous emission spectra of complex semiconductor structures that were developed in our laboratory. Transversely emitted spontaneous emission spectra over a wide range of carrier densities have been obtained for GRIN-SCH-MQW InxGa1-xAsyP1-y structures consisting of three tensile and three compressive wells. Information from these measurements and materials parameters were used to estimate carrier density for each well and subsequently used in the calculation of the emission spectra. The theoretical results were obtained by calculating the spontaneous emission rate for each well independently and then by summing over the six wells. We first calculate the band structure from a 6x6 Luttinger-Kohn Hamiltonian and find the spontaneous emission rate using the carrier density obtained from experimental measurements. A comparison between the Markovian (Lorentzian) and non-Markovian (Gaussian) line shape functions is established, considering the bandgap renormalization. We show that the Gaussian broadening function gives better agreement with the experimental data.
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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 | 0.001 | 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".