Characterization of Low Dark-Current Lateral Amorphous-Selenium Metal-Semiconductor-Metal Photodetectors
Bibliographic record
Abstract
We report a lateral amorphous-selenium (a-Se) metal-semiconductor-metal photodetector with a blocking contact. The blocking contact, a polyimide layer, is shown to significantly reduce the dark current even at high applied biases that result in high photo-to-dark-current ratios, thus leading to wide dynamic range and high signal-to-noise ratio. The use of the polyimide blocking contact prevents the injection of both holes and electrons and improves considerably upon the dark current of previously reported lateral a-Se detectors. The presence of charge trapping at the polyimide/a-Se interface is found to be negligible through the use of pulsed light experiments. The effects of electrode spacing and electrode width on device performance are investigated through experiment and simulation for device optimization. From the devices that are fabricated, it is found that the dark current is strongly dependent on the comb fingers density while the same trend is not observed for the photocurrent. It is found that the device with 10- μm electrode spacing and 10-μm electrode width has the best performance in terms of photo and dark current. This paper demonstrates the promise of low-cost lateral a-Se devices for use in indirect conversion large area digital medical X-ray imaging applications, such as chest radiography, real-time fluoroscopy, and cone beam computed tomography.
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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 | 0.000 |
| Open science | 0.001 | 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".