Use of Pulse-Height Spectroscopy to Characterize the Hole Conduction Mechanism of a Polyimide Blocking Layer Used in Amorphous-Selenium Radiation Detectors
Bibliographic record
Abstract
We demonstrate the use of pulse-height spectroscopy (PHS) to extract the internal electric field of an amorphous-selenium (a-Se) detector having a polyimide (PI) blocking layer. PHS enables more accurate measurement of the internal electric field of the detector because single-photon interactions in radiation detectors do not distort the internal electric field significantly. We fabricated a set of a-Se detectors, each having a PI layer with different thicknesses, and measured instantaneous electric field within the a-Se and PI layers using PHS, as well as the dark current of each detector. We also investigated the detector response under X-ray pulse illumination to determine the optimal thickness of PI necessary to achieve the best photo-to-dark-current ratio for low radiation dose imaging applications. Finally, we represent an analytical model of the steady-state dark-current behavior and a hole conduction mechanism in PI, which incorporates the Poole-Frankel emission model, and compared our model with the experimental results. The PHS approach reported in this article can enable the selection and design of an optimal blocking layer material when integrated with the existing [e.g., a-Se and cadmium zinc telluride (CZT)] and emerging direct-conversion radiation semiconductors (e.g., PbO and TIBr).
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".