Lateral amorphous selenium metal-semiconductor-metal photodetector for large-area high-speed indirect-conversion medical imaging applications
Bibliographic record
Abstract
Thick amorphous selenium (a-Se) as an excellent photoconductor has been used in direct conversion X-ray imaging modalities such as mammography. However, due to substantial charge trapping, such detectors experience a long X-ray response time and as a result, suffer from a slow speed of operation. Therefore, its deployment to speed-required applications such as real-time fluoroscopy remains a challenge. In this work, we aim to investigate a lateral a-Se MSM photodetector as an indirect conversion X-ray imager and its utilization in high speed, high energy medical applications. The dark current density of the newly-fabricated detector is below 20 pA/mm2 for a 200 μm×50 μm pixel pitch at electric field strengths ranging from 6 to 12 V/μm. The photoresponsivity reaches up to 2.3A/W towards blue wavelength of 468 nm at an electric field strength of 20 V/μm. Furthermore, the photocurrent has a fast speed of photoresponse, demonstrating rise time, fall time and time constant of 50 μs, 60 μs and 30 μs, respectively. Given that low dark current and high photoresponsivity this detector holds, coupled with fast photoresponse, it is believed that lateral a-Se MSM photodetector is promising for indirect conversion X-ray imager integrated with either CMOS or TFT arrays.
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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) | 0.002 | 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".