An Improved 2-D Photon Detection Probability Model for Single-Photon Avalanche Diodes With Experimental Calibrations
Bibliographic record
Abstract
Single-photon avalanche diodes (SPADs) enable ultrasensitive photodetection while being compatible to cost-effective standard CMOS technology. However, accurate characterization of their photon detection probability (PDP) remains underexplored, limiting the optimization of SPAD performance in standard CMOS processes. This work presents an efficient and robust PDP modeling approach that accounts for key process-dependent and experimental nonidealities. The model considers effects of interlayer dielectric (ILD), intermetal dielectric (IMD), and passivation layers, capturing their impact on optical transmission and PDP. To address experimental limitations, the simulated PDP is calibrated using measured transmission spectra of bandpass filters (BPFs). A Monte Carlo (MC) method further optimizes the PDP model with the consideration of process variations, enabling improved agreement with measurements. The proposed model achieves a mean absolute error (MAE) of 2.74% over the spectrum from 400 to 660 nm and an absolute difference of 0.44% at peak PDP at 420 nm when compared to the measured PDP results.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".