Effect of oscillator strength and intermediate resonance on the performance of resonant phonon-based terahertz quantum cascade lasers
Bibliographic record
Abstract
We experimentally investigated the effect of oscillator strength (radiative transition diagonality) on the performance of resonant phonon-based terahertz quantum cascade lasers that have been optimized using a simplified density matrix formalism. Our results show that the maximum lasing temperature (Tmax) is roughly independent of laser transition diagonality within the lasing frequency range of the devices under test (3.2–3.7 THz) when cavity loss is kept low. Furthermore, the threshold current can be lowered by employing more diagonal transition designs, which can effectively suppress parasitic leakage caused by intermediate resonance between the injection and the downstream extraction levels. Nevertheless, the current carrying capacity through the designed lasing channel in more diagonal designs may sacrifice even more, leading to electrical instability and, potentially, complete inhibition of the device's lasing operation. We propose a hypothesis based on electric-field domain formation and competition/switching of different current-carrying channels to explain observed electrical instability in devices with lower oscillator strengths. The study indicates that not only should designers maximize Tmax during device optimization but also they should always consider the risk of electrical instability in device operation.
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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 | 0.001 |
| 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".