Enhancing temporal performance of a-Se detectors using a low-temperature hole-blocking bilayer design
Bibliographic record
Abstract
Over the past few decades, amorphous selenium (a-Se) X-ray detectors have gained widespread use in mammography due to their remarkable spatial resolution capabilities. However, these devices encounter challenges in applications requiring lower radiation exposures and dynamic imaging, such as dynamic mammography tomosynthesis. The potential enhancement of sensitivity and temporal performance by increasing the applied voltage across the selenium layer is counteracted by concerns about dark current. Furthermore, certain applications necessitate the placement of a low-temperature hole-blocking layer on the top surface of a-Se to enable high-voltage mode in hole collecting mode. Although the use of SU-8 as a top layer has demonstrated satisfactory temporal performance, there remains room for improvement. Additionally, the use of SU-8 at the bottom has revealed interface compatibility issues. In this research, we address the compatibility challenges associated with the positioning of the SU-8 layer at the bottom by introducing a novel bilayer configuration. This bilayer setup was evaluated in both top and bottom positions. Our results indicate that when the bilayer configuration is located at the top, it combines the strengths of both layers, merging the high signal level in a Cs-doped a-Se device with the low dark current characteristics of the SU-8 layer. Notably, among the samples incorporating hole-blocking layers, the bilayer positioned at the top exhibits the most favorable lag performance, measuring below 0.5% after the 7th exposure, and a sensitivity reduction of 14% after 20 exposures (equivalent of 0.175 Gy). Moreover, employing this bilayer arrangement at the bottom enhances sensitivity by 16.1% compared to devices utilizing only the SU-8 hole-blocking layer. This improvement underscores the effective mitigation of interface challenges through the utilization of Cs-doped a-Se when SU-8 is placed at the bottom.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".