Noise Reduction in Charge-Sensitive Amplifiers for X-Ray Imagers With Large Line Capacitance
Bibliographic record
Abstract
Substantial parasitic data-line capacitance$C_{p}$in thin-film-transistor-based active-matrix flat-panel X-ray imagers limits the noise performance, bandwidth, and linearity of the analog front-end (AFE) integrated circuit, normally comprised of a charge-sensitive amplifier (CSA) to process pC-level input signals, followed by a lowpass filter. To mitigate the adverse effect of$C_{p}$on noise, we develop and implement, for the first time for X-ray-imager AFEs, two techniques based on introducing a low-noise auxiliary amplifier (AUX) to the AFE that improves noise performance without a significant increase in power consumption. In the first technique, we design the AUX to introduce a negative capacitance that neutralizes$C_{p}$. In the second, we design the AUX to provide a feedforward path around the CSA that cancels the CSA noise. In both approaches, the AUX determines the overall AFE noise performance, allowing the CSA to be designed to meet relaxed bandwidth, gain error, and linearity specifications. To demonstrate the performance of our proposed noise-reduction techniques, we present our design and experimental characterization of a single-channel AFE in a 1.8-V-supply 180-nm mixed-signal CMOS technology. Experimental results from our prototype chip indicate that both methods provide at least$2.2\times $reduction in input-referred noise at the cost of no more than a 29% increase in static-power consumption compared to a conventional AFE.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".