A Monolithic Amorphous-Selenium/CMOS Visible-Light Imager With Sub-9-μm Pixel Pitch and Extended Full-Well Capacity
Bibliographic record
Abstract
We present a monolithic hybrid visible-light imager composed of an amorphous selenium (a-Se) photoconductor thin film integrated on a CMOS active pixel sensor (APS) array readout integrated circuit. We achieve more than 14-dB signal-to-noise ratio under monochromatic light at an illuminance of 0.04 lux in one quadrant of the imager containing 7.8×7.8-μ m2pixels using a 1.6-s exposure (integration) time without operating the a-Se sensor in avalanche mode. Long integration times are made possible by exploiting the low dark-current of a 20-μm-thick a-Se sensor layer and by implementing dark-current reduction techniques in the APS, such as series stacking reset and protection devices. In addition, use of a relatively thick 20-μm a-Se layer obviates the need for an additional planarization step of the sensing layer as was done with a previously-reported avalanche-mode a-Se/CMOS imager that incorporated a 0.5-μm-thick sensing layer. We also demonstrate an improved APS design, with 7.8 × 8.7- μ m2dimensions, containing a programmable in-pixel integration capacitor to provide enhanced full-well capacity (FWC) for a-Se/CMOS imagers. Measured results show that our APS achieves a FWC of approximately 230 ke-, which is comparable to several previously-reported photodiode-based CMOS imagers with extended FWC. Our imager can also resolve an effective object size of approximately 12.5μm, demonstrated through measurement of the modulation transfer function.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".