Low dark current and high dynamic range a-Si:H MSM photodetector for large area medical imaging
Bibliographic record
Abstract
Previously a-Si:H metal-semiconductor-metal (MSM) lateral detectors for indirect medical imaging applications had been proposed by our research group. These lateral detectors are attractive due to their ease of fabrication primarily because there is no p+ doped semiconductor layer, thus making it compatible with industry standard amorphous silicon thin film transistor electronics processing. However the earlier devices exhibited high dark current which is problematic for integration mode imaging. In the other words, they were limited in term of dynamic range. In this study, we demonstrate an a-Si:H MSM lateral structure with low dark current, high dynamic range and comparable sensitivity and quantum efficiency to conventional p-i-n photodiodes. These improvements are achieved by the introduction of a thin polymer layer as a blocking contact. The fabricated amorphous silicon based MSM detector exhibits a photo-response of more than 3 orders of magnitude to a green light source (λ = 525nm). In comparison to vertical p-i-n structures, the reported MSM lateral devices show gains in terms of dynamic range, ease of fabrication (no p+ layer), faster speed at the cost of a slightly reduced quantum efficiency. The experimental results of dark and photocurrent measurements as well as the responsivity for two in-house fabricated MSM structures at different bias voltages and light intensity are presented. This results are promising and encourage the development of a-Si:H lateral MSM devices for indirect conversion large area medical imaging applications and especially low cost flat panel 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 | 0.000 |
| 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".