Bibliographic record
Abstract
The quantum cascade laser (QCL) is a semiconductor heterostruc- ture using inter sub-band transitions to generate stimulated emission. The quantum cascade detector (QCD) is a similar to the QCL, but the heterostructure is tailored to absorb radiation and give a read-out current. In this work, three planned or realised QCL:s and two QCD:s have been simulated and analysed using a program based on the non- equilibrium Green’s function theory technique (NEGFT) and com- paring to experimental measurements. The importance of electron- electron scattering for thermalisation has been phenomenologically studied by altering the barrier deformation potential and a planned QCL has been optimised to give twice the gain from the original struc- ture. The work has involved corporations with experimental groups at the National Research Council in Ottawa and the University of Wa- terloo, Canada, which resulted in an article published in the Journal of Applied Physics[1]. A new way to display the global behaviour of a QCL in terms of carrier concentration and density of states, by using the spectral func- tion has been developed. For the first time, a QCD has been simulated by NEGFT to give space- and energy-resolved carrier concentrations, density of states and energies of the electronic states. The agreement of NEGFT simulations to experiment is also anal- ysed. The model applies very well to many structures, but the lack of electron-electron interaction causes problems with thermalisation for some structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".