Amorphous silicon p-i-n photodetector with Frisch grid for high-speed medical imaging
Bibliographic record
Abstract
In indirect digital x-ray detectors, photodetectors such as hydrogenated amorphous silicon (a-Si:H) p-i-n photodetectors are used to convert the optical photons generated by the scintillating material to collectible electron-hole pairs. A problem that arises during the collection of the charges is that the mobility and lifetime of both types of carriers (electrons and holes) differ. In a-Si:H, the mobility of holes is much lower than that of electrons which leads to depth-dependent signal variations and causes the charge collection time to be extensive. It has been shown that the use of a Frisch grid can reduce the effect of the slower carriers in direct x-ray detectors. The Frisch grid is essentially a conducting grid that shields carriers from the collecting electrode until they are in close proximity. When the pixel electrodes are properly biased, the grid prevents the slow moving carriers (traveling away from the collecting electrode) from being collected and puts more weight on the fast moving carriers, thus allowing the total charge to be collected in less time. In this paper we investigate the use of a Frisch grid in a-Si:H p-i-n photodetectors for indirect x-ray detectors. Through simulations and theoretical analysis we determine the grid line sizes and positioning that will be most effective for practical p-i-n photodetector designs. In addition we compare the results of photodetectors with and without the grid to characterize the improvement achievable.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".