Photocurrent Modeling and Detectivity Optimization in a Resonant-Tunneling Quantum-Dot Infrared Photodetector
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Bibliographic record
Abstract
Theoretical modeling of the photocurrent and detectivity optimization of a resonant-tunneling quantum-dot infrared photodetector (RT-QDIP) based on nonequilibrium Green's function (NEGF) is presented. The interaction with light used in the model is based on the first-order dipole approximation and the Fermi golden rule, which is used to obtain the transition rates due to photon emission or absorption. The bound states of the QD are obtained by solving numerically the eigenvalue problem of the Hamiltonian of the QD, while the continuum states are obtained from the retarded Green's function. The in-scattering and out-scattering self-energy functions due to photon interactions are calculated from the total transition rate and the quasi-Fermi level of the QD. The Green's functions of the QDs are obtained by numerically solving their governing kinetic equations using the method of finite differences. A quantum transport equation using the Green's functions is formed to calculate the dark and photocurrent. The model has been applied to simulate the dark current and responsivity of the RT-QDIP at different temperatures and applied biases. The simulated dark current and responsivity with this model are in good agreement with experimental results. The model was used to study the effect on the dark current and the responsivity resulting from changing the QD doping density and the barrier separation between QD layers. The detectivity is obtained for different design parameters. The model used is general and can be used as a tool for the design and prediction of the dark and photocurrent characteristics of different QDIP.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it