Development of solid‐state avalanche amorphous selenium for medical imaging
Bibliographic record
Abstract
PURPOSE: Active matrix flat panel imagers (AMFPI) have limited performance in low dose applications due to the electronic noise of the thin film transistor (TFT) array. A uniform layer of avalanche amorphous selenium (a-Se) called high gain avalanche rushing photoconductor (HARP) allows for signal amplification prior to readout from the TFT array, largely eliminating the effects of the electronic noise. The authors report preliminary avalanche gain measurements from the first HARP structure developed for direct deposition onto a TFT array. METHODS: The HARP structure is fabricated on a glass substrate in the form of p-i-n, i.e., the electron blocking layer (p) followed by an intrinsic (i) a-Se layer and finally the hole blocking layer (n). All deposition procedures are scalable to large area detectors. Integrated charge is measured from pulsed optical excitation incident on the top electrode (as would in an indirect AMFPI) under continuous high voltage bias. Avalanche gain measurements were obtained from samples fabricated simultaneously at different locations in the evaporator to evaluate performance uniformity across large area. RESULTS: An avalanche gain of up to 80 was obtained, which showed field dependence consistent with previous measurements from n-i-p HARP structures established for vacuum tubes. Measurements from multiple samples demonstrate the spatial uniformity of performance using large area deposition methods. Finally, the results were highly reproducible during the time course of the entire study. CONCLUSIONS: We present promising avalanche gain measurement results from a novel HARP structure that can be deposited onto a TFT array. This is a crucial step toward the practical feasibility of AMFPI with avalanche gain, enabling quantum noise limited performance down to a single x-ray photon per pixel.
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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) | 0.001 | 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".