Direct Time-of-Flight TCSPC Analytical Modeling Including Dead-Time Effects
Bibliographic record
Abstract
The optimization of a TCSPC system requires modeling which considers the design specifications and parameters of the target application under different operating scenarios. Since single-photon detection is fundamentally a stochastic process, extensive behavioral Monte Carlo simulations are normally used. Their accuracy depends upon computation time. However, the trend towards larger SPAD arrays and emerging complex TDC sharing architectures requires much faster simulation methods. In this paper, a simple, fast and accurate analytical model is presented to address this need. It accounts for dead time effects which result in missed photon counts through the analysis of inhomogeneous continuous time Markov chain. The effective received power and photon detection rate are determined and the corresponding analytical histogram is created. This histogram is the basis for calculating time of flight and can be used to explore architectural alternatives and accelerate design verification. Outputs of the presented analytical model match those of Monte Carlo simulations, and are produced considerably faster. The computation time improvement grows with array sizes and this enables parametric analysis of TCSPC system.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".