Toward Scintillator High‐Gain Avalanche Rushing Photoconductor Active Matrix Flat Panel Imager (<scp>SHARP</scp>‐<scp>AMFPI</scp>): Initial fabrication and characterization
Bibliographic record
Abstract
Purpose We present the first prototype Scintillator High‐Gain Avalanche Rushing Photoconductor Active Matrix Flat Panel Imager ( SHARP ‐ AMFPI ). This detector includes a layer of avalanche amorphous Selenium (a‐Se) ( HARP ) as the photoconductor in an indirect detector to amplify the signal and reduce the effects of electronic noise to obtain quantum noise‐limited images for low‐dose applications. It is the first time avalanche a‐Se has been used in a solid‐state imaging device and poses as a possible solution to eliminate the effects of electronic noise, which is crucial for low‐dose imaging performance of AMFPI . Methods We successfully deposited a solid‐state HARP structure onto a 24 × 30 cm 2 array of thin‐film transistors ( TFT array) with a pixel pitch of 85 μm. The HARP layer consists of 16 μm of a‐Se with a hole‐blocking and electron‐blocking layer to prevent charge injection from the high‐voltage bias and pixel electrodes, respectively. An electric field ( E S e ) up to 105 V μm −1 was applied across the a‐Se layer without breakdown. A 150 μm thick‐structured CsI:Tl scintillator was used to form SHARP ‐ AMFPI . The x‐ray imaging performance is characterized using a 30 kV p Mo/Mo beam. We evaluate the spatial resolution, noise power, and detective quantum efficiency at zero frequency of the system with and without avalanche gain. The results are analyzed using cascaded linear system model ( CLSM ). Results An avalanche gain of 76 ± 5 was measured at E S e = 105 V μm −1 . We demonstrate that avalanche gain can amplify the signal to overcome electronic noise. As avalanche gain is increased, image quality improves for a constant (0.76 mR ) exposure until electronic noise is overcome. Our system is currently limited by poor optical transparency of our high‐voltage electrode and long integrating time which results in dark current noise. These two effects cause high‐spatial frequency noise to dominate imaging performance. Conclusions We demonstrate the feasibility of a solid‐state HARP x‐ray imager and have fabricated the largest active area HARP sensor to date. Procedures to reduce secondary quantum and dark noise are outlined. Future work will improve optical coupling and charge transport which will allow for frequency DQE and temporal metrics to be obtained.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".