Characterization of bias induced metastability of amorphous silicon thin film transistor based passive pixel sensor switch and its impact on biomedical x-ray imaging application
Bibliographic record
Abstract
Active Matrix Flat Panel Imagers (AMFPIs) based on amorphous silicon (a-Si:H) thin film transistor (TFT) array is the most promising technology for large area biomedical x-ray imaging. a-Si:H TFT exhibits a metastable shift in its characteristics when subject to prolonged gate bias that results in a change in its threshold voltage (VΤ) and a corresponding change in ON resistance (RON). If not properly accounted for, the VΤ shift can be a major constraint in imaging applications as it contributes to the fixed pattern noise in the imager. In this work, we investigated the timedependent shift in VΤ (ΔVΤ) of a-Si:H TFTs stressed with the same bipolar pulsed bias used for static (chest radiography, mammography, and static protein crystallography) and real time imaging (low dose fluoroscopy at 15, 30 and 60 frames/second, and dynamic protein crystallography). We used the well known power law model of time dependent ΔVT to estimate the change in RON over time. Our calculation showed that RON can be decreased ~ 0.03 % per frame and ~ 5 % over 10,000 hours at 30 frames/second. We verified the theoretical results with measurement data. The implication of TFT metastability on the performance (NPS, and DQE) of biomedical imagers is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".