The dependence of the modulation transfer function on the blocking layer thickness in amorphous selenium x‐ray detectors
Bibliographic record
Abstract
Blocking layers are used to reduce leakage current in amorphous selenium detectors. The effect of the thickness of the blocking layer on the presampling modulation transfer function (MTF) and on dark current was experimentally determined in prototype single-line CCD-based amorphous selenium (a-Se) x-ray detectors. The sampling pitch of the detectors evaluated was 25 microm and the blocking layer thicknesses varied from 1 to 51 microm. The blocking layers resided on the signal collection electrodes which, in this configuration, were used to collect electrons. The combined thickness of the blocking layer and a-Se bulk in each detector was approximately 200 microm. As expected, the dark current increased monotonically as the thickness of the blocking layer was decreased. It was found that if the blocking layer thickness was small compared to the sampling pitch, it caused a negligible reduction in MTF. However, the MTF was observed to decrease dramatically at spatial frequencies near the Nyquist frequency as the blocking layer thickness approached or exceeded the electrode sampling pitch. This observed reduction in MTF is shown to be consistent with predictions of an electrostatic model wherein the image charge from the a-Se is trapped at a characteristic depth within the blocking layer, generally near the interface between the blocking layer and the a-Se bulk.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".