Noise optimization of an active pixel sensor for real-time digital x-ray fluoroscopy
Bibliographic record
Abstract
In this paper, we derive the input referred noise in terms of the on-pixel transistor device dimensions of the main noise sources of our array, namely, the flicker noise of the pixel thin-film transistors (TFTs), and the reset noise. Theoretical calculations and simulation results show that it is desirable to minimize the amplifier TFT gate dimensions, L<sub>1</sub> and W<sub>1</sub>, and to maximize the read-out TFT gate width, W<sub>2</sub>. Noise curves are presented as a function of transistor dimensions, allowing the designer to choose appropriate device dimensions when designing flat-panel imaging circuits. In addition, it is demonstrated how the optimal amplifier TFT gate width, W<sub>1</sub>, for the lowest-noise design, changes as a function of the extraneous sense node capacitance. The noise simulations indicate that with proper device dimension design, it is possible to achieve sub-500 electron input referred noise performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".