Comparative Performance Evaluation of Conventional and Folded Detector Structures: Application to Perovskite X-ray Detectors
Bibliographic record
Abstract
The imaging performance of a semiconductor radiation imaging detector critically depends on its photoconductor layer thickness. The conventional detector structure (i.e., a photoconductor layer is sandwiched between two parallel electrodes) needs a strict design criterion on photoconductor thickness as compared to folded detector structure for optimizing the detective quantum efficiency (DQE), which is the most important imaging performance. In this paper, the DQE performance of both folded and conventional detector structures is analyzed by incorporating the quantum noise due to random charge carrier trapping in the photoconductor layer in the cascaded linear system model. An analytical expression for the variance of incomplete charge collection in folded structure is also developed. The optimum values of photoconductor layer thickness and spacing between electrodes for maximizing the DQE under various combinations of exposure, electronic noise and charge carrier transport parameters are investigated. The folded structure provides a design flexibility for achieving DQE higher than 0.7 by adjusting the distance between electrodes without compromising the quantum efficiency while the maximum possible DQE in conventional structure can be even below 0.3 for certain values of material and detector parameters.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".