The implementation and spectrum response analysis of multi-finger photogate APS pixels
Bibliographic record
Abstract
Photogate APS pixels use a MOS capacitor created potential well to capture photocarriers. However, optical absorption of the poly-silicon gate reduces photon transmission. We investigate multi-fingered photogates with openings in the gate to increase photon collection. 0.18 μm CMOS standard and multi-fingered photogates were implemented where the enclosed detection area is divided by 1, 3 and 5 poly fingers. Preliminary response comparison with standard photogates suggested the sensitivity of 1-finger pixels dropped ~22% implying open areas collected 62% of the photocarriers. The sensitivity of 3 and 5 finger pixels increased ~33 - 49% over standard, with open area collection ~170 - 290% more photocarriers due to fringing field created potential wells. These results indicated at least 66% of the incident light is absorbed by the poly-silicon gate. In spectral response multi-fingered pixels showed an increase in sensitivity in the red (631 nm) - yellow (587 nm) - green (571 nm) wavelengths but a relative decline in the blue (470 nm) possibly due to more absorption in the Silicon Nitride insulator layers. Some Silicon Nitride (SixNy) compositions have higher absorption coefficients in the Blue than poly-silicon and thus may dominate the absorption in these photogates structures. Extended analysis on the potential well formation in the multi-fingered photogates was perform using 2-D device simulation. Simulated multi-fingered photogates showed the strength of the fringing field increased as the open area spacing between poly-fingered decreases; with the 5-finger having a nearly uniform depleted region over the entire photogate area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".