Theoretical modeling of dark current in quantum dot infrared photodetectors using nonequilibrium Green’s functions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A theoretical model describing electron dynamics in quantum dot (QD) infrared photodetectors (QDIPs) is presented. The model is based on the nonequilibrium Green’s functions formalism which provides a general framework to study electron transport in a nonequilibrium quantum system and in the presence of interactions. A self-consistent solution of the charge density and the average potential energy through the device and satisfying Poisson’s equation has been obtained; hence, the Hamiltonian of the QDs is established. The self-energies due to coupling with the contact layers and due to internal electron interactions are calculated and then 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 Green’s functions is formed to calculate the current. The model has been applied to simulate the dark current and to extract microscopic information about the density of states and carrier distribution in the quantum dot bound and continuum states. The simulated dark currents with this model are in good agreement with experimental results over a wide range of applied biases and temperatures. The model was also used to study the effect on the dark current and the average number of electrons occupying the QDs due to changing the QD doping density, the barrier separation between QD layers, and the number of QD layers. The model is general and can be applied to any QDIP structures as a tool in design and for predictions of their dark current characteristics.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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