Experimental Demonstration of the Small Pixel Effect in an Amorphous Photoconductor using a Monolithic Spectral Single Photon Counting Capable CMOS-Integrated Amorphous-Selenium Sensor
Bibliographic record
Abstract
We directly demonstrate, for the first time, the small pixel effect in an amorphous photoconductor by measuring the intrinsic transient response of an amorphous selenium (a-Se) photoconductor pixel of various sizes monolithically combined with a CMOS sensor. Our front-end circuitry leverages the lowest electronic noise reported for aSe photon detection, $\sim 300 \mu \text{V}_{rms}$ measured, to demonstrate the first-ever fully CMOS integrated a-Se pulse heigh spectroscopy results. Fabricated using $0.18 \mu \text{m}$ CMOS mixedsignal technology, the chip contains four pixel arrays with 30, 60, 90, and $120 \mu \text{m}$-pitch, each pixel having its own dedicated charge-sensitive amplifier. Our measured results from a monoenergetic Gamma source demonstrate the capability of a-Se/CMOS pixel arrays to achieve a high count rate when the small pixel effect is leveraged in the high spatial resolution pixel arrays. The results demonstrated can expedite the development of energy discriminating single photon counting imaging detectors for large area mammography tomosynthesis and dedicated breast computed tomography, something that has not as yet been achieved at the commercial scale.
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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 | 0.001 | 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".