Study of gain phenomenon in lateral metal-semiconductor-metal detectors for indirect conversion medical imaging
Bibliographic record
Abstract
Previously, metal-semiconductor-metal (MSM) lateral amorphous selenium (a-Se) detectors have been proposed for indirect detector medical imaging applications. These detectors have raised interest due to their high-speed and photogain. The gain measured from these devices was assumed to have been photoconductive gain; however the origin of this gain was not fully understood. In addition, whether or not there was any presence of photocurrent multiplication gain was not investigated. For integration-type applications photocurrent multiplication gain is desirable since the total collected charge can be greater than the total number of absorbed photons. In order to fully appreciate the value of MSM devices and their benefit for different applications, whether it is counting or integration applications, we need to investigate the responsible mechanisms of the observed response. In this paper, we systematically study, through experimental and theoretical means, the nature of the photoresponse and its responsible mechanisms. This study also exposes the possible means to increase the performance of the device and under what conditions it will be most beneficial.
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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".