Spectral function and responsivity of resonant tunneling and superlattice quantum dot infrared photodetectors using Green’s function
Bibliographic record
Abstract
Theoretical modeling of resonant tunneling (RT) and superlattice (SL) quantum dot infrared photodetectors (QDIPs) using Green’s function is reported. The RT QDIP gives very low dark current which improves the detectivity of the device and allows for high temperature operation. The SL QDIP gives high responsivity and is suitable for low-level signal detection. The theoretical model is based on Green’s function method which is used to calculate the spectral function and the density of states of the two detectors. The kinetic equation that governs Green’s functions is solved numerically using the method of finite differences. From the information obtained from the density of states, the possible energy transitions are obtained. The bound states are calculated by solving the eigenvalue problem using the method of finite differences, while the continuum states localized in the quantum dot region are calculated using Green’s functions. Using the first order dipole approximation and Fermi golden rule, the eigenstates are used to calculate the responsivity of the detectors which is compared with available experimental results. The theoretical model is then used for studying the effect of changing the quantum dot height-to-diameter ratio on the normal incidence responsivity of the SL structure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".