The Effect of Pinned Photodiode Shape on Time-of-Flight Demodulation Contrast
Bibliographic record
Abstract
An empirical investigation on improving the pinned photodiode (PPD) demodulation contrast by tailoring the geometry of the device is presented. Results of this TCAD simulation-based study are used to develop a structure especially suited for time-of-flight applications. In order to obtain a fair comparison between various PPD shapes, a square structure is adopted as a benchmark and all subsequent PPD geometries use the same process parameters. Five different PPD shapes are compared: 1) nominal square-shaped PPD; 2) triangular PPD; 3) constant-field PPD; 4) L-shaped constant-field PPD; and 5) proposed PPD. Device physics simulations are undertaken and the speed of each structure is evaluated on the basis of its demodulation contrast. It is shown that triangular and constant-field PPDs can provide significant improvement compared with a conventional square-shaped PPD, however they still lack effective lateral charge transfer in the final electron sorting stage. The final PPD proposed in this paper achieves this with a tailored PPD shape and doping gradient. In addition, the transfer gates are placed close to one another to make use of gate-induced fringe fields and thus improve the speed of electron sorting. Using these techniques, a PPD demodulation contrast of 61% is obtained at a frequency of 100 MHz, which is comparable to the contrast achieved in state-of-the-art photogate-based designs.
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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 | 0.003 |
| 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 | 0.000 |
| 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".