Dark current mechanisms in stabilized amorphous selenium based n-i detectors for x-ray imaging applications
Bibliographic record
Abstract
The dark current behavior under operating bias is one of the important selection criteria for an x-ray photoconductor to be usable in a practical x-ray image detector. The authors have developed an analytical model for describing the transient and steady-state behavior of dark current in n-i-type amorphous selenium (a-Se) detectors by considering carrier injections from the metal contacts and thermally generated carriers. It has been found that the thermal generation current is almost two orders of magnitude smaller than the total steady-state dark current in n-i-type a-Se detectors. The main source of dark current is the injection of holes from the metal/n-layer interface which is described by the diffusion theory. The hole injection from the metal depends on the blocking layer (n-layer) thickness, the concentration of trap centers in the blocking layer, the characteristic carrier release time, and the effective barrier height. The fitting of the first principles model with the experimental results estimates the concentration of deep hole trap center in the n-layer, the trap depth from the valence band edge, and the effective barrier heights for the injecting carriers. The electron injection varies with the work function of the contact metal.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".