Photoconductor selection for digital flat panel x-ray image detectors based on the dark current
Bibliographic record
Abstract
We consider a flat panel direct conversion x-ray image detector in which a photoconductor is used to directly convert absorbed x-ray photons to collectable electron hole pairs. The storage capacitor at each pixel stores a quantity of charge proportional to the incident radiation. The charge stored at each pixel is read out every Δt seconds. Based on a condition for a minimal dynamic constraint, we have examined the corresponding range of constraints on the material properties to establish approximate x-ray photoconductor selection criteria. We consider requirements for radiology and fluoroscopy in which Δt=1 and 1/30 s, respectively. When the electrical contacts are totally blocking, the dark current is limited by bulk thermal generation. Minimum band gap Eg requirements depend on the concentration of defects Nd around the Fermi level and their recombination cross section Sr. For single crystal photoconductors with a neutral-defect concentration of the order of ∼1014 cm−3, we need Eg>1.5 eV for fluoroscopy and Eg>1.7 eV for radiology. For high purity single crystals, these band gap values can be lower, for example, Eg≈1.42 eV (GaAs) requires Nd<1012 cm−3. In the case of amorphous semiconductors, the dark current is due to the emission of carriers from a distribution of localized states in the energy gap. For a maximum integrated concentration of ∼1016 cm−3 of localized states around the Fermi level, calculations suggest Eg>1.75 eV for fluoroscopy and Eg>1.9 eV for radiology; amorphous selenium readily satisfies these conditions. When the dark current is controlled to thermal emission from the metal electrode into the semiconductor over a potential barrier φB (i.e., bulk thermal generation of carriers is negligible) then we find that the required Schottky barrier φB can be expressed in terms of the band gap Eg. For Schottky barriers φB∼0.6Eg, Eg>1.8 eV for fluoroscopy and >2.0 eV for radiology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".