Enhanced sensitivity achievement using advanced device simulation of multifinger photo gate active pixel sensors
Bibliographic record
Abstract
A 2-dimensional device simulation of Multi finger active pixel sensors is investigated for obtaining enhanced pixel sensitivity. Photo gate APS use a MOS capacitor that can capture incident illumination with a potential well created under the photo gate. The major drawback of such a technology is the absorption of shorter wavelength by the polysilicon gate resulting in a higher sensitivity in the red visible spectrum than in the blue range. In our previous work we implemented 0.18μm CMOS standard and multi fingered photo gate design where the enclosed detection area is divided by 3, 5 and 7 fingers. The experimental results showed that fringing field created potential wells for the 3 and 5 finger photo gate designs have 1.7 times higher collection of photo carriers over the standard photo gate. The device simulation showed that fringing fields from the edges of the poly gates created potential wells that fully covered the open silicon areas allowing light conversion without the optical absorption in the poly silicon gates. Extending simulations to 0.5 μm, 0.25 μm and 0.18 μm multifinger poly gates showed that the fringing fields stayed the same width as the gates shrunk, so that as the number of fingers increased the potential well in the open areas became more uniform. The device sensitivity based on the potential well locations, and previous experimental results, suggested peak efficiencies for the 0.5 μm design as 7 fingers, 0.25 μm at 9 fingers and 0.18 μm at 11 fingers. Peak efficiency was projected to be 2.2 times that of a standard photogate.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".